KR101200368B1 - Climate change vulnerability assessment system and method - Google Patents

Abstract

PURPOSE: A climate change vulnerability evaluation system and a method for the same are provided to support local governments to set segmented items of climate change adaptation policies. CONSTITUTION: A climate change vulnerability evaluation method includes the following steps: climate change variables are segmented and saved; sensitivity variables, which represent the influence of the climate change variables, are segmented and saved; adaptation capability variables for reducing the influence of the climate change variables and sensitivity variables are segmented and saved; boundary data for administrative districts is saved; respective climate change variables, sensitivity variables, and adaptation capability variables for respective administrative districts are obtained; proxy variables for the respective climate change variables, sensitivity variables, and adaptation capability are saved; weighted values for the respective climate change variables, sensitivity variables, and adaptation capability and for the proxy variables are saved; the proxy variable is standardized; vulnerability is obtained using the standardized proxy value and the weighted values; the vulnerability is standardized; vulnerabilities are compared by administrative district; and the variables, vulnerabilities, and information of the administrative districts are displayed. [Reference numerals] (AA) Evaluation item selection(preparation tasks:developing the standardization methodology of vulnerability); (BB) Top-down methodology; (CC) Bottom-top methodology; (EE) Downscaling; (GG) Climate data deduction according to 6 scenarios; (HH) Data establishment for manufacturing proxy variables; (II) Expert interview and reference research; (JJ) Proxy variable selection; (KK) Proxy variable weighed value setting; (LL) Delphi analysis(first time, second time); (MM) Proxy variable standardization and vulnerability index calculation; (NN) Standardization and vulnerability evaluation formula application; (OO) Vulnerability index calculation; (PP) Vulnerability evaluation result mapping(applying to cities in the whole country)

Description

Climate Change Vulnerability Assessment System and Method {.}

The present invention relates to a climate change vulnerability assessment system and method thereof.

In the present invention, the Climate Change Vulnerability Assessment was conducted nationwide by using the Climate Change adaptation toolkit based on GIS (GIS).

In addition, the inventors of the seven sectors of health, forest, water management, ecosystem, agriculture, marine / fishing, and disaster set up detailed substitute variables and set weights, and then used CCGIS to evaluate the detailed items selected for each category. We performed a vulnerability assessment and analyzed the vulnerable municipalities.

From the results of the analysis, local governments can recognize areas and urgency that need to reinforce adaptive capacity in the first place, and visual results (vulnerability maps) were derived through CCGIS to improve the understanding of local government officials and various stakeholders of local governments.

Therefore, the present invention is the first attempt in Korea in terms of standardized vulnerability assessment of national sectors in each sector, and in each sector, the local governments will be used as a policy tool to determine the priority vulnerable sectors in the future adaptation plan. It is expected to be.

In addition, since the present inventors can present objective criteria when establishing detailed implementation plans for adaptation to climate change adaptation to the local government through climate change vulnerability assessment, it is expected to identify factors that affect climate change vulnerability and reduce risk exposure and contribute to improvement of adaptive capacity.

The frequency and intensity of extreme climatic phenomena caused by climate change cause not only short-term environmental changes but also long-term temperature rises, precipitation changes, and sea level rises, which are currently being explained through scientific observations (Ministry of Environment, 2010). .

In the last 100 years (1906 ~ 2005), the global average temperature has risen about 0.74 ℃, and in Korea, the average temperature has risen 1.7 ℃ over the past 100 years.

The Intergovernmental Panel on Climate Change (IPCC) predicts that the global average temperature will increase by about 2.8 ° C over the A1B scenario over the next 100 years, and about 4 ° C in Korea.

The IPCC recommended the importance of forecasting climate change due to climate change in the future and adapting measures to minimize the adverse effects of climate change that current and future generations will face.

Responses to climate change are largely divided into mitigation and adaptation.

Mitigation through GHG emission reduction alone is limited in reducing the impact of climate change, so the aspect of adaptation to changing climate should also be strengthened.

In this regard, the Stern Report (2006) found that even if all GHG emissions are stopped now, a temperature rise of about 1 ° C is inevitable. Economic analysis of climate change in Korea II (Ministry of Environment, 2011) suggests that The invention resulted in economic damages of 2800 trillion won.

After introducing the concept of adaptation for the first time in the 3rd comprehensive climate change policy (2005 ~ 2007), 13 ministries including the Ministry of Environment jointly announced the National Climate Change Adaptation Plan (2008). In line with the implementation of the Framework Act on Low Carbon, Green Growth (April 4, 2010), the National Measures on Climate Change Adaptation (2011-2015) were established.

Here, national climate change adaptation measures are the first statutory national plans, including health, disasters / disasters, agriculture, forests, marine and fisheries, water management, ecosystems, climate change monitoring and forecasting, adaptation industries, education and public relations, and international cooperation. 87 subjects were included.

According to Article 38 of the Enforcement Decree of the Framework Act on Low Carbon, Green Growth, the head of the local government should establish a local green growth promotion plan (detailed implementation plan) every five years based on the national strategy and the five-year plan.

Accordingly, local governments should establish detailed implementation plans at the local government level based on national adaptation measures, and in particular, adaptation plans reflecting regional characteristics should be established as subjects of adaptation measures for climate change.

The impact of climate change varies from region to region, and adaptation measures at the local level, which understand and understand the characteristics of the region, are very important.However, in the case of local governments, in order to establish detailed implementation plans for adaptation, there is a lack of budget and manpower. As there is a limit, support from the central government level is needed.

Therefore, the central government can provide direct manpower input, increase budget, and provide tools. In particular, it is necessary to provide vulnerability assessment tools for the professional development of local governments and further support.

The present invention relates to a climate change vulnerability assessment system and method thereof.

In the present invention, the Climate Change Vulnerability Assessment was conducted nationwide by using the Climate Change adaptation toolkit based on GIS (GIS).

In addition, the inventors of the seven sectors of health, forest, water management, ecosystem, agriculture, marine / fishing, and disaster set up detailed substitute variables and set weights, and then used CCGIS to evaluate the detailed items selected for each category. We performed a vulnerability assessment and analyzed the vulnerable municipalities.

From the results of the analysis, local governments can recognize areas and urgency that need to reinforce adaptive capacity in the first place, and visual results (vulnerability maps) were derived through CCGIS to improve the understanding of local government officials and various stakeholders of local governments.

Therefore, the present invention is the first attempt in Korea in terms of standardized vulnerability assessment of national sectors in each sector, and in each sector, the local governments will be used as a policy tool to determine the priority vulnerable sectors in the future adaptation plan. It is expected to be.

In addition, since the present inventors can present objective criteria when establishing detailed implementation plans for adaptation to climate change adaptation to the local government through climate change vulnerability assessment, it is expected to identify factors that affect climate change vulnerability and reduce risk exposure and contribute to improvement of adaptive capacity.

According to the characteristics of the present invention for achieving the above object, the climate exposure variable, which is a variable representing the impact of climate change, health, forest, ecosystem, agriculture, water management, marine and fisheries and A climate exposure variable storage unit for storing the grid unit by breaking down the disaster sector; Sensitivity variables, which are variables representing the degree of impact by climate exposure variables, are divided into health, forest, ecosystem, agriculture, water management, marine and fisheries, and disaster sectors and stored in grid units. part; Adaptation capacity that can reduce the effects of climate exposure and sensitivity variables is divided into health sector, forest sector, ecosystem sector, agriculture sector, water management sector, marine / fishery sector and disaster sector and stored in grid units. Capability variable storage; An administrative district storage unit for storing boundary data of the administrative district; Referring to the administrative boundary boundary data stored in the administrative region storage unit, the climate exposure variable for each administrative region is generated through the climate exposure variable stored in the climate exposure variable storage unit, and the administration is performed through the sensitivity variable stored in the sensitivity variable storage unit. A city and district data conversion unit for generating a sensitivity variable for each zone and generating an adaptive capability variable for each administrative region through the adaptive capability variable stored in the adaptive capability variable storage unit; A surrogate variable storage unit for storing climate-exposure variables for each administrative region, sensitivity variables for each administrative region, and adaptive capability variables for each administrative region generated by the city and district data conversion unit; Weights for climate exposure variables, sensitivity variables, adaptability variables and detailed substitutions within each surrogate variable to assess municipal vulnerability by health sector, forest sector, ecosystem sector, agriculture sector, water management sector, marine and fisheries sector and disaster sector. A weight storage unit for storing a weight for the variable; A substitute variable standardizing unit for deriving a substitute variable standardization value by standardizing the substitute variable stored in the substitute variable storage unit; A vulnerability index calculator for calculating a vulnerability index by applying a weight stored in the weight storage unit to a substitute variable standardized value derived from the substitute variable standardization unit; A vulnerability index standardization unit for deriving a vulnerability index standardization value by standardizing a vulnerability index calculated by the vulnerability index calculation unit; A vulnerability standardization value comparing unit comparing the vulnerability index standardized value standardized by the vulnerability index standardizing unit for each administrative region; Display administrative district geographic information from the administrative boundary boundary data stored in the administrative zone storage unit, climate exposure variable, administrative sensitivity parameter, adaptive capacity variable or vulnerability index of each administrative zone, stored in the surrogate variable storage unit, And a display unit for displaying the vulnerability index standard value together with the administrative district geographic information.

로 계산되고, 상기 변수 자료는 기후노출 변수, 민감도 변수 또는 적응능력 변수이고, 상기 행정구역별 변수 자료는 행정구역별 기후노출 변수, 행정구역별 민감도 변수 또는 행정구역별 적응능력 변수인 것을 특징으로 한다.The city and district data conversion unit stores the climate exposure variable, the sensitivity variable, and the adaptive capacity variable in a grid unit into the climate exposure variable for each administrative region, the sensitivity variable for each administrative region, and the adaptive capability variable for each administrative region. The predicted data value (C (i)) per grid from the climate exposure variable stored in the unit, the sensitivity variable stored in the sensitivity variable storage unit, or the adaptive capacity variable stored in the adaptability variable storage unit, and the administrative region stored in the administrative region storage unit. From the boundary data, for each grid area (S (i)) overlapping each administrative district and the number N of grids overlapping each administrative district, the variable data for each administrative district Y (i) ,

The variable data are climate exposure variables, sensitivity variables or adaptive capacity variables, and the variable data for each administrative area are climate exposure variables for each administrative area, sensitivity variables for each administrative area, or adaptive capacity variables for each administrative area. do.

In addition, the weight storage unit, through a second Delphi survey for the health sector, forest sector, ecosystem sector, agriculture sector, water management sector, marine and fisheries sector, and disaster sector, can be applied to surrogate variables of climate exposure, sensitivity and adaptability. It is characterized in that the weight and the weight for the substitutive variables in each category are applied.

The surrogate variable normalization unit may be configured to normalize the surrogate variables to a value having a range of 0 to 1 using a normalization formula.

It is characterized by that.

In addition, the vulnerability index is a difference between the climate impact variable and climate change variable composed of climate exposure and sensitivity variables,

(α, β, γ means weight).

In addition, the vulnerability index standardization unit, characterized in that for standardizing the vulnerability index to a value having a range of 0 ~ 1 using a standardized formula, the standardized formula is

It is characterized by that.

In addition, the health section is divided into health vulnerability items due to floods, health vulnerability items due to typhoons, health vulnerability items due to heat waves, health vulnerability items due to cold waves, health vulnerability items due to rising ozone concentrations, and fine dust. Health Vulnerability Items by, Air Vulnerability Items by Other Air Pollutants, Infectious Disease Health Vulnerability Items by Insects and Rodents, Health Vulnerability Items by Waterborne Mediated Diseases, (mm), climate exposure variables including the number of days with more than 80 mm of daily precipitation, flooding area (ha) due to flooding, population over 65 (people), population under 13 (people), elderly living alone (Total population) (%), the proportion of basic beneficiaries (%), lowland area below 10m (km2), lowland households below 10m (households), flood damage population (persons), waterborne disease number (persons) Number, gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), health center workforce per population (persons / ten thousand), emergency medical institutions per population (dog / million), GRDP health care And the adaptive capacity variable including social welfare service (million won), wherein the health vulnerability items caused by the typhoon include the daily maximum precipitation (mm), the number of times the daily precipitation is 80 mm or more, and the maximum daily wind speed is 14 m. Climate exposure variables, including the number of days above / s, population over 65 (people), population under 13 (people), elderly living alone (% of total population), basic living beneficiaries (%), Lowland area below 10m (km2), Lowland household below 10m (household), Sensitivity variable including number of waterborne diseases (person), Gross Domestic Product (GRDP) (million won), Financial Independence (%), Health Insurance Coverage Ratio (%), Health center workforce per population (persons / ten thousand), number of emergency medical institutions per population (dog / million), GRDP health care and social services ( 10,000 yen), the health vulnerability item due to heat waves, the highest daily temperature (℃), the number of times (day) when the daily maximum temperature is 33 ℃ or more, the day the minimum temperature is 25 ℃ or more Climate exposure variables, including the number of times (times), sensory temperature (℃), discomfort index (-), heat wave index (-), and relative humidity (%), population over 65 (people), population under 13 (people) ) Sensitivity variable including the elderly living alone (total population) (%), the proportion of basic beneficiaries (%), the number of deaths from cardiovascular disease (persons), and the number of deaths from heat stroke / heatstroke (GDP), regional total production (GRDP) (KRW Million), Financial Independence (%), Health Insurance Covered Population (%), Health Center Personnel Per Population (Person / Men), Emergency Medical Institutions per Population (Unit / Million), GRDP Health and Social Services (KRW Million) Including the adaptive capacity variable, the health vulnerability items due to the cold wave, the number of times (day), the day average temperature is below zero Climate exposure variables, including the number of days when the temperature is below zero (times), the maximum number of consecutive precipitation-free days (times), the number of days (day) with a daily maximum wind speed of 14 m / s or more, and snow cover (cm), 65 Population older than 13 years (person), 13 years old or younger (person), elderly person living alone (total population) (%), basic living recipient ratio (%), the number of patients with respiratory disease hospitalization (person), cerebrovascular disease death toll (person) , Adaptive capacity, gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), workforce per population (persons / ten thousand), number of emergency medical institutions per population (unit / million), Including the GRDP health and social welfare services (million won), adaptive capacity variables, and the health vulnerability items due to the increase in ozone concentration, daily maximum temperature (℃), the number of times (times) when the time ozone concentration is 100ppb or more, Climate exposure variables, including the number of days of ozone advisory (days), the number of days (eight) of 8-hour cumulative ozone concentrations above 60 ppb / 8 hr, Sensitivity including (person), population 13 years or younger (person), elderly person living alone (total population) (%), basic living recipient ratio (%), respiratory disease inpatient number (person), cardiovascular death toll (person) Variables, Regional Gross Domestic Product (GRDP) (million won), Financial Independence (%), Health Insurance Covered Population (%), Health Center Workforce (P / M), Number of Emergency Medical Services per Population (P / M), GRDP And social adaptation service variable (million won), including the adaptive ability variable, and the health vulnerability item by the fine dust, daily maximum temperature (℃), fine dust concentration (ug / ㎥), time fine dust concentration is 100ug / Climate exposure variables including the number of times (m) over m3, population over 65 (people), population under 13 (people), elderly living alone (% of total population), basic living pensioners (%), respiratory organs Sensitivity variables, including number of patients admitted to the disease (persons), deaths from cardiovascular disease (persons), gross domestic product (GRDP) (million won), financial independence (%), Adaptability capacity variables, including health coverage coverage (%), health center workforce per population (persons / million), number of emergency medical institutions per population (units / million), GRDP health care and social services (million won) Health vulnerability items caused by other air pollutants include daily maximum temperature (℃), CO (external residential emissions) (kg), CO (industrial emissions) (kg), SOx (external residential emissions) (kg), SOx Climate exposure variables including (industrial emissions) (kg), NOx (household emissions) (kg), NOx (industrial emissions) (kg), population 65 and older, population 13 and older, Sensitivity variables including elderly people living alone (total population) (%), basic living beneficiaries (%), respiratory disease inpatients (persons), cardiovascular deaths (persons), regional GDP (million won), Financial Independence (%), Health Insurance Covered Population (%), Number of Health Center Personnel per Population (Person / Million), Number of Emergency Medical Institutions per Population (Unit / Million), GRDP Health Service and Social Welfare Service Including the adaptive capacity variable including ups (million won), the infectious disease health vulnerability items by insects and rodents, the number of times (day) the day maximum temperature is 33 ℃ or more, the number of days the day minimum temperature is 25 ℃ or more Climate exposure variables, including daily maximum rainfall (mm), number of days (80) with daily precipitation greater than 80 mm, population aged 65 or older, population aged 13 or older, and percentage of elderly living alone (total population) (%), Sensitivity to basic living beneficiaries (%), average annual number of malaria cases (persons), annual average number of cases of Tsutsugamushi disease, total regional production (GRDP) (million won), financial independence ( %), Health insurance coverage ratio (%), health center workforce per population (persons / ten thousand), number of emergency medical institutions per population (dog / million), GRDP health care and social services (million won) And health vulnerability item about the waterborne mediated disease is the best Climate exposure variables including the number of days when the temperature is above 33 ° C (times), the number of days when the minimum temperature is 25 ° C or more (times), the maximum daily precipitation (mm), and the number of times when the daily precipitation is more than 80 mm (times) , Sensitivity variables including population aged 65 and older (people), population aged 13 and younger (people), elderly living alone (total population) (%), basic living pensioners (%), number of waterborne diseases (persons), within Total Production (GRDP) (KRW Million), Financial Independence (%), Health Insurance Covered Population (%), Health Center Personnel per Population (Person / Men), Emergency Medical Institutions per Population (Unit / Million), GRDP Health and Social Services It is characterized by including the adaptive capacity variable including (million won).

In addition, the forest sector is classified into landslide items due to heavy rains, vulnerability items to forest roads caused by landslides, vulnerability items to forest fires, vulnerability items to pine trees by pests, vulnerability items to pine and pine mushrooms, and forest productivity. Vulnerability items, items of vulnerability of forest vegetation due to drought, the landslide items due to the heavy rain, the number of times (day), daily maximum precipitation amount (mm), precipitation in June-August ( Mm), climate exposure variables including maximum daily rainfall (mm), average slope in forests (degrees), conifer area (ha), average elevation in forests (m), freeland area (ha) Variables, adaptation capacity variables including forest officials (persons), forest control area (ha), regional gross domestic product (GRDP) (million won), and financial independence (%). , The number of days with more than 80 mm of precipitation (Times), daily maximum precipitation (mm), June-August precipitation (mm), climate exposure variables including 5-day maximum precipitation (mm), mean slope in forest (degrees), coniferous area (ha), forest Sensitivity variables, including mean altitude (m), freeland area (ha), distance to forestry (m), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) (million won), Including the adaptive ability variable including the financial independence (%), the vulnerability items for forest fires, the number of times (day) when the daily maximum temperature is 33 ℃ or more, the number of times (day) the daily maximum wind speed is more than 14m / s ), Climate exposure variables including the number of days with effective humidity less than or equal to 35%, the maximum number of consecutive un rainfall days, conifer area (ha), deciduous tree area (ha), and mixed forest area (ha) , Soil moisture 10 cm (m³ / m³), average slope (degrees) in forests, sensitivity variables including total population (persons), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) (million won) ), Ash Including the adaptability variable including the degree of independence (%), the vulnerability items of the pine tree caused by the pests are: precipitation (mm) in June-August (summer season), daily minimum temperature (℃) in June-August (summer season) ), Climate exposure variables including daily maximum temperature (℃) in June-August (summer), number of days (maximum) with wind speeds of 14 m / s, area of pine (m²), average slope in forest ( Degrees), average altitude in forests (m), sensitivity variables including pest incidence area (ha), number of forest officials (persons), pest control employees (persons), forest control area (ha), regional total production (GRDP) ) (Million KRW), financial independence (%), including the adaptive capacity variable, the pine and pine mushroom vulnerability items, June-August precipitation (mm), June-August daily average temperature (℃), Climate exposure variables, including rainfall (mm / day), soil moisture 10 cm (m³ / m³), maximum number of consecutive rains-free days (times), area of pine (m²), average elevation in forest (m), forest By-product Sensitivity variables including tons, adaptation variables including forest officials (persons), forest control area (ha), regional gross domestic product (GRDP) (million won), financial independence (%), and Vulnerability items in forest productivity include climate exposure variables, including contaminant area (ha), precipitation (mm / day), daily maximum temperature (° C), daily minimum temperature (° C), and the maximum number of consecutive days of no precipitation. ), Sensitivity variable including deciduous tree area (ha), mixed forest area (ha), number of forest officials (person), forest control area (ha), natural rest year implementation area (m²), natural forest nursery (ha), financial independence (%), Including the adaptive capacity variable, which includes the Gross Domestic Product (GRDP) (million won), and the vulnerability of forest vegetation due to drought includes: precipitation (mm / day), the maximum number of consecutive precipitation free days ( Climate exposure variable, conifer area (ha), deciduous tree area (ha), mixed forest surface including the number of days (effectiveness) of effective humidity less than 35% (ha), sensitivity variables including plantations (ha), number of forest officials (persons), forest control area (ha), regional gross production (GRDP) (million won), financial independence (%), natural forest nursery (ha It is characterized by including the adaptive capacity variable including).

In addition, the ecosystem section includes the items of fragility of conifers (vulnerabilities of tree growth and distribution), the items of insect fragility, and the vulnerability of national parks (vulnerabilities of conservation zone management), and the vulnerability of the conifers (trees). Vulnerability of growth and distribution) items are: January-March daily average temperature (° C), temperature (° C), precipitation (mm / day), June-August daily average temperature (° C), June-August Climate exposure variables including daily maximum temperature (° C), conifer distribution area (ha), coniferous wood production (㎥), coniferous forest by-product production (㎥), forest-related population (persons), timber felling area (ha) Sensitivity variables, including the number of agricultural and forestry businesses (number of people), the number of agricultural and forestry workers (persons), adaptation variables, including coniferous afforestation area (ha), natural forest nursery (ha), and number of forestry officials (persons). Including, vulnerability items of the insect, January-March daily average temperature (° C), the number of times the daily average temperature is below zero (times), April average daily Temperature (° C), average relative humidity in April (%), maximum value of consecutive un precipitation days (times), amount of evaporation (mm / day), insolation (W / m2), daily average temperature in June to August (° Climate exposure variables including C), forest pests (number), forest pathogens-Fusarium blight (number), bee farmers (dog), bee breeding scale (bucket), number of insect-borne infectious diseases (persons), pest damage Sensitivity variables including harvesting area (ha), pest damage harvesting amount (㎥), pest control time-small pears (accumulated), forest control area (ha), pest control employees (persons), number of bioindustry (dogs) , Adaptability variables including eco-friendly orchards (households), eco-friendly orchards (households), and the vulnerability of the national park (vulnerabilities in conservation zone management) items include precipitation (mm / day), continuous precipitation Maximum number of days (times), number of days with daily average temperature below zero (times), number of times with daily maximum temperature above 33 ° C (times), March-May precipitation (mm / da y), June-August precipitation (mm / day), September-November precipitation (mm / day), December-February precipitation (mm / day), the number of times of daily precipitation more than 80mm (times), daily maximum Climate exposure variables, including the number of days with wind speeds of 14 m / s or more, the number of species of plants in the national park, the number of species of animals in the national park, the number of visitors, and the number of visitors from the previous year ( %), Sensitivity variables including plant endangered species (species), endangered species (species), number of administrative districts to be linked for national park management, national park staff (persons), national park organization (Number), national park offices (number), national park area increase / decrease, national park commentary operation frequency (times), national park temple area (㎢), natural rest year implementation area (㎡), natural rest year It is characterized in that it comprises an adaptive capability variable including the implementation distance (km).

In addition, the agricultural sector, including the items of vulnerability of farmland soil erosion, items of vulnerability of cultivation / rearing facility collapse, items of vulnerability of rice productivity, items of vulnerability of apple productivity, items of vulnerability of livestock productivity. The vulnerable items of erosion include precipitation (mm / day), the number of days (80) of days with more than 80 mm of precipitation, the number of days of precipitation (more than 10 mm) (times), field exposure (ha), paddy fields Sensitivity variables including area (ha), regional average slope (deg), number of farm machinery possessed per acreage (number / ha), number of farmers / total farmers utilizing PC, number of agricultural population per acreage (number of land / ha), maintenance project per land area The vulnerable items of the cultivation / rearing facility collapse include adaptability variables including employees (ha / ha), gross domestic product per capita (GRDP) (million won), and financial independence (%). number of days per second or more, snowfall 20 cm or more Climate exposure variables including the number of days (times), the number of days with more than 160 mm of precipitation, the plant crop area (ha), the breeding area (m²), the house damage area per plant crop area, and breeding Sensitivity variables including the number of livestocks per plant area (area / ha), number of agricultural population per person / haul, area / haul, PC-use farmers / total farmers, maintenance staff per farmland (m / ha), 1 Including the adaptive capacity variable including the GDP per capita (GRDP) (million won) and the degree of financial independence (%), the vulnerability item of rice productivity is the number of days in which the minimum temperature is 13 ° C or lower from April to June. ), The number of times when the minimum temperature is 17 ℃ or less (times), the number of times the minimum temperature is 14 ℃ or less (times), the days when the maximum temperature is 30 ℃ or more The number of times (times), the number of times the time when the ozone concentration is more than 100 ppb in April-October, the log (the sum of the amount of insolation in April-October), Climate exposure variables including the number of times, the number of days with a maximum wind speed of 14 m / s or more, the rice paddy area (ha), the crop area per crop area, the sensitivity variable including the potential for pest damage, and the rice fields per cultivated area. Production (ton / ha), rice seedlings per cultivated area (person / ha), arable land ratio (%), number of farmers using PC, total farmers, maintenance business associates per cultivated land (person), gross domestic product per capita (GRDP) (Million won), including the adaptive capacity variable including financial independence (%), the vulnerability of apple productivity is the annual average temperature range of 8 ℃ ~ 11 ℃, October daily average temperature (℃), April-August Daily average temperature (℃), August average daily temperature (℃), April-August daily maximum temperature (℃), April-October precipitation (mm), April-October daily maximum wind speed of 14m / s or more Climate exposure parameters including the number of times, apple growing area (ha), sensitivity variable including crop damage area per crop, apple yield per cultivated area (kg / 10a), cultivated area Number of main employees per employee (person / ha), number of farm machinery holdings per apple cultivation area (large / ha), number of farmers / apple farmers utilizing PC, maintenance business associates (person) in arable land area, total GDP per capita (GRDP) (KRW 1 million) ), Including the ability to adapt to variables including financial independence (%), the vulnerability of livestock productivity, the number of times (day) the day maximum daily temperature is 27 ℃ or more, the number of times (temperature) the temperature and humidity index is 72 or more , Climate exposure variables including the number of days with more than 20 cm of snowfall, the number of days with a maximum wind speed of more than 14 m / s, the number of livestock breeding heads (thousands), and the number of livestock's deaths per animal area ha), sensitivity variables including livestock disease risk, livestock main worker per capita (ha / ha), livestock wastewater treatment capacity, number of farmers / livestock farmers utilizing PC, maintenance staff (m / ha) per arable land area, Adaptability, including regional GDP per capita (KRW million) and financial independence (%) In that it comprises a number of features.

In addition, the water management section includes the details of the vulnerability items of dimensions, the items of weakness of completion, the items of vulnerability to water quality and aquatic ecology, and the items of vulnerability of the dimensions, the maximum daily precipitation amount (mm), the daily precipitation amount Climate exposure variables, including the number of days above 80 mm, the maximum amount of precipitation over a five-day period (mm), ground runoff (mm / day), and precipitation from June to September (mm), lowland area below 10 m, Low-density households less than 10 m (households), dike use area (%), population density (person / 명), total population, regional average slope (deg), road area ratio (%), flood damage in the past three years (thousand won), recent Sensitivity variable including flood damage population (number of people) for 3 years, financial independence (%), number of civil servants per population (person / million), gross domestic product (GRDP) (million won), water management officials per area (person / ㎢) ), Adaptive capacity variables including river yield (%), domestic drainage capacity (㎥ / min), reservoir flood control capacity (million㎥), Sex items include the maximum number of consecutive days of no precipitation, December-February precipitation (mm), March-May precipitation (mm), December-February evaporation (mm), March-May evapotranspiration (mm) ), Climate exposure variables including underground runoff (mm / day), population density (person / ㎢), total population (person), water supply: 1 liter per day, grain production per ton (ton / ㎢), per area Livestock production (mari / ㎢), groundwater consumption (㎥ / year), river water consumption (㎥ / year), domestic water use (1,000㎥ / year), industrial water use (1,000㎥ / year), agricultural water use (1,000㎥ Sensitivity variables, including (years), financial independence (%), the number of civil servants per population (persons / ten thousand), gross domestic product (GRDP) (million won), the number of land management officials (persons), water supply rate (%), Includes adaptive capacity variables including groundwater capacity (1,000㎥ / year), reservoir storage capacity for water supply per unit area (1,000㎥), water reuse capacity per unit area (1,000㎥ / year), and Vulnerability item is highest temperature (℃), daily maximum steel (Mm), the number of days with more than 80 mm of precipitation, the maximum number of consecutive un precipitation days, the number of times with the highest daily temperature above 33 ° C, and the number of days with the lowest minimum temperature above 25 ° C. Climate exposure variables including the number of times, regional average slope (deg), river yield (%), livestock industry population (persons / million), livestock production status (mari / 마리), fertilizer use per arable land (ton / ha), major animal species distribution (species), major plant species distribution (species), forest area percentage (%), sensitivity variables including managed land rate (%), sewerage rate (%), and civil servants per population (person) Per million people), population density (%), road area ratio (%), road length per unit area (km).

In addition, the marine / fisheries sector, the details include the vulnerability items of fisheries (aquaculture) according to the temperature change, the vulnerability items of fisheries (aquaculture) according to the temperature change, sea level temperature (℃), annual sea water temperature Rate of rise (℃ / yr), number of days with daily average temperature below freezing, number of days with daily high temperature above 33 ℃, number of times with daily precipitation greater than 80mm, times of occurrence of jellyfish damage Climate exposure variables, including aquaculture, aquaculture farming area (fish-sea cage) (ha), aquaculture farming facility area (business-sea cage) (ha), aquaculture farming facility area (fish-festival ceremony) (ha ), Sensitivity variables including farming facilities area (business-festival) (ha), fish farming status (sea cage) (point), fish farming status (festival) (point), financial independence (%), population The number of civil servants (persons / ten thousand), aquaculture farming facility area (fish-land tank) (ha), aquaculture farming area (business-land tank) (ha), It characterized by including the adaptability containing eoga into expression (land sujosik) (places).

In addition, the disaster category includes, in detail, an infrastructure vulnerability item for flooding, an infrastructure vulnerability item for heatwaves, an infrastructure vulnerability item for heavy snowfall, and an infrastructure vulnerability item for sea level rise. Infrastructure vulnerability items include the number of times the daily precipitation is 80 mm / day or more, the climate exposure variable including the daily maximum precipitation in mm, and the road area of the flood-vulnerable area (number of flooded buildings) (m2). ), Water supply area (㎡), electricity supply area (㎡), gas supply facility area (㎡), heat supply facility area (㎡), oil storage and oil supply facility area (㎡), sewage area (㎡), water quality Sensitivity variables including pollution prevention facility area (㎡), adaptive capacity variables including regional GDP per capita (GRDP) (million won), number of civil servants per population (persons / ten thousand), river yield (%) , The infrastructure vulnerability items for the heat wave, the highest temperature is 33 ℃ Sensitivity variables including the number of days above (times), the number of days above the minimum temperature of 25 ° C, the road area (㎡) of heat-vulnerable areas (impermeable area) (㎡), within the per capita area Adaptability capacity variables, including gross production (GRDP) (million won), civil servants per population (persons / million), per capita land area (㎡), and the infrastructure vulnerability category for heavy snowfall is the amount of snowfall (cm). Climate exposure variables included, road surface area (㎡) in vulnerable areas of heavy snowfall (1,000 won), sensitivity area including railroad area (㎡), airport area (㎡), gross domestic product per capita (GRDP) (million won) ), Including the adaptive capacity variable including the number of civil servants per population (persons / ten thousand), and the infrastructure vulnerability items for sea level rise, climate exposure variables including the annual rate of increase in seawater temperature, sea level rise vulnerable areas ( Includes road area (㎡), port area (㎡), water pollution prevention facility area (㎡) of soft rising rate (%) Is characterized by including the ability to adapt the parameters, including the sensitivity variables, the number official per per region GDP (GRDP) (millions), population (patients / million), aiding equipment area (㎡).

According to the characteristics of the present invention for achieving the object as described above, (a) climate exposure variables, which are variables representing the effects of climate change, health, forest, ecosystem, agriculture, water management, marine / fisheries and disasters Subdividing the data into grids and storing the data in grid units; (b) subdividing the sensitivity variable, which is a variable representing the degree of influence by the climate exposure variable, into health, forest, ecosystem, agriculture, water management, marine / fishing, and disaster sectors and storing them in grid units; (c) storing the adaptive capacity variables that can reduce the influence of the climate exposure variable and the sensitivity variable in the unit of health, forest, ecosystem, agriculture, water management, marine / fishing, and disasters by grid unit; (d) storing the boundary data of the administrative area; (e) creating a climate exposure variable for each administrative region using the climate exposure variable stored in step (a) with reference to the administrative boundary data stored in step (d), and generating the sensitivity variable stored in step (b). Generating a sensitivity variable for each administrative region through the generation, and generating an adaptive capability variable for each administrative region through the adaptive capability variable stored in the step (c); (f) storing the climate exposure variable for each administrative region, the sensitivity variable for each administrative region, and the adaptive capability variable for each administrative region generated by step (e); (g) Weights for climate exposure variables, sensitivity variables, and adaptive capacity variables and detailed surrogate variables within each surrogate variable to assess municipal vulnerability in each sector; health, forest, ecosystem, agriculture, water management, marine / fish and disasters. Storing the weight for the; (h) standardizing surrogate variables stored in step (f) to derive surrogate variable normalized values; (i) calculating a vulnerability index by applying the weight stored in step (g) to the surrogate variable normalized value derived in step (h); (j) deriving a vulnerability index standard value by standardizing the vulnerability index calculated in step (i); (k) comparing the vulnerability index standardized values standardized in step (j) for each administrative district; (l) displaying administrative district geographic information from the administrative boundary boundary data stored in step (d); and climate exposure variables, administrative sensitivity parameters, and adaptive capacity variables for each administrative zone; And displaying a vulnerability index and a vulnerability index standardized value together with the administrative district geographic information.

로 계산되고, 상기 변수 자료는 기후노출 변수, 민감도 변수 또는 적응능력 변수이고, 상기 행정구역별 변수 자료는 행정구역별 기후노출 변수, 행정구역별 민감도 변수 또는 행정구역별 적응능력 변수인 것을 특징으로 한다.In addition, in the step (e) of converting the climate exposure variable, the sensitivity variable and the adaptive capacity variable in the grid unit into the climate exposure variable for each administrative region, the sensitivity variable for each administrative region and the adaptive capability variable for each administrative region, the (a) Prediction data values per grid (C (i)) and administrative zone boundaries stored in steps (d) from climate exposure variables stored in steps, sensitivity variables stored in steps (b), or adaptability variables stored in step (c) For the area S (i) for each grid overlapping each administrative zone from the data, and the number N of each grid overlapping each administrative zone, the variable data Y (i) for each administrative zone are:

The variable data are climate exposure variables, sensitivity variables or adaptive capacity variables, and the variable data for each administrative area are climate exposure variables for each administrative area, sensitivity variables for each administrative area, or adaptive capacity variables for each administrative area. do.

In addition, in step (g), the exposure of climate exposure, sensitivity, and adaptability through two-step Delphi investigations by health, forest, ecosystem, agriculture, water management, marine and fisheries, and disasters It is characterized in that the weights for the variables and the weights for the substitutive variables in each category are derived and applied.

In addition, the step (h), characterized in that to standardize the surrogate variables using a standardized formula to a value having a range of 0 ~ 1, the standardized formula is

It is characterized by that.

In addition, the vulnerability index is a difference between the climate impact variable and climate change variable composed of climate exposure and sensitivity variables,

(α, β, γ means weight).

In addition, the step (j), characterized in that the standardized by using a standardized equation to the value of the vulnerability index in the range of 0 ~ 1,

It is characterized by that.

In addition, the health section is divided into health vulnerability items due to floods, health vulnerability items due to typhoons, health vulnerability items due to heat waves, health vulnerability items due to cold waves, health vulnerability items due to rising ozone concentrations, and fine dust. Health Vulnerability Items by, Air Vulnerability Items by Other Air Pollutants, Infectious Disease Health Vulnerability Items by Insects and Rodents, Health Vulnerability Items by Waterborne Mediated Diseases, (mm), climate exposure variables including the number of days with more than 80 mm of daily precipitation, flooding area (ha) due to flooding, population over 65 (people), population under 13 (people), elderly living alone (Total population) (%), the proportion of basic beneficiaries (%), lowland area below 10m (km2), lowland households below 10m (households), flood damage population (persons), waterborne disease number (persons) Number, gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), health center workforce per population (persons / ten thousand), emergency medical institutions per population (dog / million), GRDP health care And the adaptive capacity variable including social welfare service (million won), wherein the health vulnerability items caused by the typhoon include the daily maximum precipitation (mm), the number of times the daily precipitation is 80 mm or more, and the maximum daily wind speed is 14 m. Climate exposure variables, including the number of days above / s, population over 65 (people), population under 13 (people), elderly living alone (% of total population), basic living beneficiaries (%), Lowland area below 10m (km2), Lowland household below 10m (household), Sensitivity variable including number of waterborne diseases (person), Gross Domestic Product (GRDP) (million won), Financial Independence (%), Health Insurance Coverage Ratio (%), Health center workforce per population (persons / ten thousand), number of emergency medical institutions per population (dog / million), GRDP health care and social services ( 10,000 yen), the health vulnerability item due to heat waves, the highest daily temperature (℃), the number of times (day) when the daily maximum temperature is 33 ℃ or more, the day the minimum temperature is 25 ℃ or more Climate exposure variables, including the number of times (times), sensory temperature (℃), discomfort index (-), heat wave index (-), and relative humidity (%), population over 65 (people), population under 13 (people) ) Sensitivity variable including the elderly living alone (total population) (%), the proportion of basic beneficiaries (%), the number of deaths from cardiovascular disease (persons), and the number of deaths from heat stroke / heatstroke (GDP), regional total production (GRDP) (KRW Million), Financial Independence (%), Health Insurance Covered Population (%), Health Center Personnel Per Population (Person / Men), Emergency Medical Institutions per Population (Unit / Million), GRDP Health and Social Services (KRW Million) Including the adaptive capacity variable, the health vulnerability items due to the cold wave, the number of times (day), the day average temperature is below zero Climate exposure variables, including the number of days when the temperature is below zero (times), the maximum number of consecutive precipitation-free days (times), the number of days (day) with a daily maximum wind speed of 14 m / s or more, and snow cover (cm), 65 Population older than 13 years (person), 13 years old or younger (person), elderly person living alone (total population) (%), basic living recipient ratio (%), the number of patients with respiratory disease hospitalization (person), cerebrovascular disease death toll (person) , Adaptive capacity, gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), workforce per population (persons / ten thousand), number of emergency medical institutions per population (unit / million), Including the GRDP health and social welfare services (million won), adaptive capacity variables, and the health vulnerability items due to the increase in ozone concentration, daily maximum temperature (℃), the number of times (times) when the time ozone concentration is 100ppb or more, Climate exposure variables, including the number of days of ozone advisory (days), the number of days (eight) of 8-hour cumulative ozone concentrations above 60 ppb / 8 hr, Sensitivity including (person), population 13 years or younger (person), elderly person living alone (total population) (%), basic living recipient ratio (%), respiratory disease inpatient number (person), cardiovascular death toll (person) Variables, Regional Gross Domestic Product (GRDP) (million won), Financial Independence (%), Health Insurance Covered Population (%), Health Center Workforce (P / M), Number of Emergency Medical Services per Population (P / M), GRDP And social adaptation service variable (million won), including the adaptive ability variable, and the health vulnerability item by the fine dust, daily maximum temperature (℃), fine dust concentration (ug / ㎥), time fine dust concentration is 100ug / Climate exposure variables including the number of times (m) over m3, population over 65 (people), population under 13 (people), elderly living alone (% of total population), basic living pensioners (%), respiratory organs Sensitivity variables, including number of patients admitted to the disease (persons), deaths from cardiovascular disease (persons), gross domestic product (GRDP) (million won), financial independence (%), Adaptability capacity variables, including health coverage coverage (%), health center workforce per population (persons / million), number of emergency medical institutions per population (units / million), GRDP health care and social services (million won) Health vulnerability items caused by other air pollutants include daily maximum temperature (℃), CO (external residential emissions) (kg), CO (industrial emissions) (kg), SOx (external residential emissions) (kg), SOx Climate exposure variables including (industrial emissions) (kg), NOx (household emissions) (kg), NOx (industrial emissions) (kg), population 65 and older, population 13 and older, Sensitivity variables including elderly people living alone (total population) (%), basic living beneficiaries (%), respiratory disease inpatients (persons), cardiovascular deaths (persons), regional GDP (million won), Financial Independence (%), Health Insurance Covered Population (%), Number of Health Center Personnel per Population (Person / Million), Number of Emergency Medical Institutions per Population (Unit / Million), GRDP Health Service and Social Welfare Service Including the adaptive capacity variable including ups (million won), the infectious disease health vulnerability items by insects and rodents, the number of times (day) the day maximum temperature is 33 ℃ or more, the number of days the day minimum temperature is 25 ℃ or more Climate exposure variables, including daily maximum rainfall (mm), number of days (80) with daily precipitation greater than 80 mm, population aged 65 or older, population aged 13 or older, and percentage of elderly living alone (total population) (%), Sensitivity to basic living beneficiaries (%), average annual number of malaria cases (persons), annual average number of cases of Tsutsugamushi disease, total regional production (GRDP) (million won), financial independence ( %), Health insurance coverage ratio (%), health center workforce per population (persons / ten thousand), number of emergency medical institutions per population (dog / million), GRDP health care and social services (million won) And health vulnerability item about the waterborne mediated disease is the best Climate exposure variables including the number of days when the temperature is above 33 ° C (times), the number of days when the minimum temperature is 25 ° C or more (times), the maximum daily precipitation (mm), and the number of times when the daily precipitation is more than 80 mm (times) , Sensitivity variables including population aged 65 and older (people), population aged 13 and younger (people), elderly living alone (total population) (%), basic living pensioners (%), number of waterborne diseases (persons), within Total Production (GRDP) (KRW Million), Financial Independence (%), Health Insurance Covered Population (%), Health Center Personnel per Population (Person / Men), Emergency Medical Institutions per Population (Unit / Million), GRDP Health and Social Services It is characterized by including the adaptive capacity variable including (million won).

In addition, the forest sector is classified into landslide items due to heavy rains, vulnerability items to forest roads caused by landslides, vulnerability items to forest fires, vulnerability items to pine trees by pests, vulnerability items to pine and pine mushrooms, and forest productivity. Vulnerability items, items of vulnerability of forest vegetation due to drought, the landslide items due to the heavy rain, the number of times (day), daily maximum precipitation amount (mm), precipitation in June-August ( Mm), climate exposure variables including maximum daily rainfall (mm), average slope in forests (degrees), conifer area (ha), average elevation in forests (m), freeland area (ha) Variables, adaptation capacity variables including forest officials (persons), forest control area (ha), regional gross domestic product (GRDP) (million won), and financial independence (%). , The number of days with more than 80 mm of precipitation (Times), daily maximum precipitation (mm), June-August precipitation (mm), climate exposure variables including 5-day maximum precipitation (mm), mean slope in forest (degrees), coniferous area (ha), forest Sensitivity variables, including mean altitude (m), freeland area (ha), distance to forestry (m), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) (million won), Including the adaptive ability variable including the financial independence (%), the vulnerability items for forest fires, the number of times (day) when the daily maximum temperature is 33 ℃ or more, the number of times (day) the daily maximum wind speed is more than 14m / s ), Climate exposure variables including the number of days with effective humidity less than or equal to 35%, the maximum number of consecutive un rainfall days, conifer area (ha), deciduous tree area (ha), and mixed forest area (ha) , Soil moisture 10 cm (m³ / m³), average slope (degrees) in forests, sensitivity variables including total population (persons), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) (million won) ), Ash Including the adaptability variable including the degree of independence (%), the vulnerability items of the pine tree caused by the pests are: precipitation (mm) in June-August (summer season), daily minimum temperature (℃) in June-August (summer season) ), Climate exposure variables including daily maximum temperature (℃) in June-August (summer), number of days (maximum) with wind speeds of 14 m / s, area of pine (m²), average slope in forest ( Degrees), average altitude in forests (m), sensitivity variables including pest incidence area (ha), number of forest officials (persons), pest control employees (persons), forest control area (ha), regional total production (GRDP) ) (Million KRW), financial independence (%), including the adaptive capacity variable, the pine and pine mushroom vulnerability items, June-August precipitation (mm), June-August daily average temperature (℃), Climate exposure variables, including rainfall (mm / day), soil moisture 10 cm (m³ / m³), maximum number of consecutive rains-free days (times), area of pine (m²), average elevation in forest (m), forest By-product Sensitivity variables including tons, adaptation variables including forest officials (persons), forest control area (ha), regional gross domestic product (GRDP) (million won), financial independence (%), and Vulnerability items in forest productivity include climate exposure variables, including contaminant area (ha), precipitation (mm / day), daily maximum temperature (° C), daily minimum temperature (° C), and the maximum number of consecutive days of no precipitation. ), Sensitivity variable including deciduous tree area (ha), mixed forest area (ha), number of forest officials (person), forest control area (ha), natural rest year implementation area (m²), natural forest nursery (ha), financial independence (%), Including the adaptive capacity variable, which includes the Gross Domestic Product (GRDP) (million won), and the vulnerability of forest vegetation due to drought includes: precipitation (mm / day), the maximum number of consecutive precipitation free days ( Climate exposure variable, conifer area (ha), deciduous tree area (ha), mixed forest surface including the number of days (effectiveness) of effective humidity less than 35% (ha), sensitivity variables including plantations (ha), number of forest officials (persons), forest control area (ha), regional gross production (GRDP) (million won), financial independence (%), natural forest nursery (ha It is characterized by including the adaptive capacity variable including).

In addition, the ecosystem section includes the items of fragility of conifers (vulnerabilities of tree growth and distribution), the items of insect fragility, and the vulnerability of national parks (vulnerabilities of conservation zone management), and the vulnerability of the conifers (trees). Vulnerability of growth and distribution) items are: January-March daily average temperature (° C), temperature (° C), precipitation (mm / day), June-August daily average temperature (° C), June-August Climate exposure variables including daily maximum temperature (° C), conifer distribution area (ha), coniferous wood production (㎥), coniferous forest by-product production (㎥), forest-related population (persons), timber felling area (ha) Sensitivity variables, including the number of agricultural and forestry businesses (number of people), the number of agricultural and forestry workers (persons), adaptation variables, including coniferous afforestation area (ha), natural forest nursery (ha), and number of forestry officials (persons). Including, vulnerability items of the insect, January-March daily average temperature (° C), the number of times the daily average temperature is below zero (times), April average daily Temperature (° C), average relative humidity in April (%), maximum value of consecutive un precipitation days (times), amount of evaporation (mm / day), insolation (W / m2), daily average temperature in June to August (° Climate exposure variables including C), forest pests (number), forest pathogens-Fusarium blight (number), bee farmers (dog), bee breeding scale (bucket), number of insect-borne infectious diseases (persons), pest damage Sensitivity variables including harvesting area (ha), pest damage harvesting amount (㎥), pest control time-small pears (accumulated), forest control area (ha), pest control employees (persons), number of bioindustry (dogs) , Adaptability variables including eco-friendly orchards (households), eco-friendly orchards (households), and the vulnerability of the national park (vulnerabilities in conservation zone management) items include precipitation (mm / day), continuous precipitation Maximum number of days (times), number of days with daily average temperature below zero (times), number of times with daily maximum temperature above 33 ° C (times), March-May precipitation (mm / da y), June-August precipitation (mm / day), September-November precipitation (mm / day), December-February precipitation (mm / day), the number of times of daily precipitation more than 80mm (times), daily maximum Climate exposure variables, including the number of days with wind speeds of 14 m / s or more, the number of species of plants in the national park, the number of species of animals in the national park, the number of visitors, and the number of visitors from the previous year ( %), Sensitivity variables including plant endangered species (species), endangered species (species), number of administrative districts to be linked for national park management, national park staff (persons), national park organization (Number), national park offices (number), national park area increase / decrease, national park commentary operation frequency (times), national park temple area (㎢), natural rest year implementation area (㎡), natural rest year It is characterized in that it comprises an adaptive capability variable including the implementation distance (km).

In addition, the agricultural sector, including the items of vulnerability of farmland soil erosion, items of vulnerability of cultivation / rearing facility collapse, items of vulnerability of rice productivity, items of vulnerability of apple productivity, items of vulnerability of livestock productivity. The vulnerable items of erosion include precipitation (mm / day), the number of days (80) of days with more than 80 mm of precipitation, the number of days of precipitation (more than 10 mm) (times), field exposure (ha), paddy fields Sensitivity variables including area (ha), regional average slope (deg), number of farm machinery possessed per acreage (number / ha), number of farmers / total farmers utilizing PC, number of agricultural population per acreage (number of land / ha), maintenance project per land area The vulnerable items of the cultivation / rearing facility collapse include adaptability variables including employees (ha / ha), gross domestic product per capita (GRDP) (million won), and financial independence (%). number of days per second or more, snowfall 20 cm or more Climate exposure variables including the number of days (times), the number of days with more than 160 mm of precipitation, the plant crop area (ha), the breeding area (m²), the house damage area per plant crop area, and breeding Sensitivity variables including the number of livestocks per plant area (area / ha), number of agricultural population per person / haul, area / haul, PC-use farmers / total farmers, maintenance staff per farmland (m / ha), 1 Including the adaptive capacity variable including the GDP per capita (GRDP) (million won) and the degree of financial independence (%), the vulnerability item of rice productivity is the number of days in which the minimum temperature is 13 ° C or lower from April to June. ), The number of times when the minimum temperature is 17 ℃ or less (times), the number of times the minimum temperature is 14 ℃ or less (times), the days when the maximum temperature is 30 ℃ or more The number of times (times), the number of times the time when the ozone concentration is more than 100 ppb in April-October, the log (the sum of the amount of insolation in April-October), Climate exposure variables including the number of times, the number of days with a maximum wind speed of 14 m / s or more, the rice paddy area (ha), the crop area per crop area, the sensitivity variable including the potential for pest damage, and the rice fields per cultivated area. Production (ton / ha), rice seedlings per cultivated area (person / ha), arable land ratio (%), number of farmers using PC, total farmers, maintenance business associates per cultivated land (person), gross domestic product per capita (GRDP) ) (In millions of dollars), including financial independence (%),

Vulnerability items of the apple productivity are yearly average temperature range 8 degrees Celsius-11 degrees Celsius, October daily average temperature (℃), April-August daily average temperature (℃), August daily average temperature (℃), April-August Climate exposure parameters including daily maximum temperature (° C), precipitation in April-October (mm), number of days (maximum) in April-October wind speed of 14 m / s, crop area (ha), crops per area Sensitivity variables including previous crop damage area, apple production per cultivated area (kg / 10a), number of fruit-runners per cultivated area (person / ha), number of farm machinery holdings per apple cultivation area (large / ha), number of farmers / apple farmers using PC, Includes adaptation capacity variables, including maintenance staff per arable land area (persons), per capita gross domestic product (GRDP) (million won), and financial independence (%). The number of times (times) above the ℃, the number of times (times) when the temperature and humidity index is 72 or more, the number of times (times) when the snow amount is 20cm or more, and the maximum daily wind speed is 14m / s or more Climate exposure variables including the number of times, livestock breeding heads (thousands), livestock livestock per litter area (number of sites / ha), sensitivity variables including livestock disease risk, and livestock livestock population per breeder area ( Persons / ha), livestock wastewater treatment capacity, number of farmers using PCs / livestock farming, number of farmland maintenance business associates (person / ha), regional GDP per capita (GRDP) (million won), financial independence (%) It is characterized by including the ability variable.

In addition, the water management section includes the details of the vulnerability items of dimensions, the items of weakness of completion, the items of vulnerability to water quality and aquatic ecology, and the items of vulnerability of the dimensions, the maximum daily precipitation amount (mm), the daily precipitation amount Climate exposure variables, including the number of days above 80 mm, the maximum amount of precipitation over a five-day period (mm), ground runoff (mm / day), and precipitation from June to September (mm), lowland area below 10 m, Low-density households less than 10 m (households), dike use area (%), population density (person / 명), total population, regional average slope (deg), road area ratio (%), flood damage in the past three years (thousand won), recent Sensitivity variable including flood damage population (number of people) for 3 years, financial independence (%), number of civil servants per population (person / million), gross domestic product (GRDP) (million won), water management officials per area (person / ㎢) ), Adaptive capacity variables including river yield (%), domestic drainage capacity (㎥ / min), reservoir flood control capacity (million㎥), Sex items include the maximum number of consecutive days of no precipitation, December-February precipitation (mm), March-May precipitation (mm), December-February evaporation (mm), March-May evapotranspiration (mm) ), Climate exposure variables including underground runoff (mm / day), population density (person / ㎢), total population (person), water supply: 1 liter per day, grain production per ton (ton / ㎢), per area Livestock production (mari / ㎢), groundwater consumption (㎥ / year), river water consumption (㎥ / year), domestic water use (1,000㎥ / year), industrial water use (1,000㎥ / year), agricultural water use (1,000㎥ Sensitivity variables, including (years), financial independence (%), the number of civil servants per population (persons / ten thousand), gross domestic product (GRDP) (million won), the number of land management officials (persons), water supply rate (%), Includes adaptive capacity variables including groundwater capacity (1,000㎥ / year), reservoir storage capacity for water supply per unit area (1,000㎥), water reuse capacity per unit area (1,000㎥ / year), and Vulnerability item is highest temperature (℃), daily maximum steel (Mm), the number of days with more than 80 mm of precipitation, the maximum number of consecutive un precipitation days, the number of times with the highest daily temperature above 33 ° C, and the number of days with the lowest minimum temperature above 25 ° C. Climate exposure variables including the number of times, regional average slope (deg), river yield (%), livestock industry population (persons / million), livestock production status (mari / 마리), fertilizer use per arable land (ton / ha), major animal species distribution (species), major plant species distribution (species), forest area percentage (%), sensitivity variables including managed land rate (%), sewerage rate (%), and civil servants per population (person) Per million people), population density (%), road area ratio (%), road length per unit area (km).

In addition, the marine / fisheries sector, the details include the vulnerability items of fisheries (aquaculture) according to the temperature change, the vulnerability items of fisheries (aquaculture) according to the temperature change, sea level temperature (℃), annual sea water temperature Rate of rise (℃ / yr), number of days with daily average temperature below freezing, number of days with daily high temperature above 33 ℃, number of times with daily precipitation greater than 80mm, times of occurrence of jellyfish damage Climate exposure variables, including aquaculture, aquaculture farming area (fish-sea cage) (ha), aquaculture farming facility area (business-sea cage) (ha), aquaculture farming facility area (fish-festival ceremony) (ha ), Sensitivity variables including farming facilities area (business-festival) (ha), fish farming status (sea cage) (point), fish farming status (festival) (point), financial independence (%), population The number of civil servants (persons / ten thousand), aquaculture farming facility area (fish-land tank) (ha), aquaculture farming area (business-land tank) (ha), It characterized by including the adaptability containing eoga into expression (land sujosik) (places).

In addition, the disaster category includes, in detail, an infrastructure vulnerability item for flooding, an infrastructure vulnerability item for heatwaves, an infrastructure vulnerability item for heavy snowfall, and an infrastructure vulnerability item for sea level rise. Infrastructure vulnerability items include the number of times the daily precipitation is 80 mm / day or more, the climate exposure variable including the daily maximum precipitation in mm, and the road area of the flood-vulnerable area (number of flooded buildings) (m2). ), Water supply area (㎡), electricity supply area (㎡), gas supply facility area (㎡), heat supply facility area (㎡), oil storage and oil supply facility area (㎡), sewage area (㎡), water quality Sensitivity variables including pollution prevention facility area (㎡), adaptive capacity variables including regional GDP per capita (GRDP) (million won), number of civil servants per population (persons / ten thousand), river yield (%) , The infrastructure vulnerability items for the heat wave, the highest temperature is 33 ℃ Sensitivity variables including the number of days above (times), the number of days above the minimum temperature of 25 ° C, the road area (㎡) of heat-vulnerable areas (impermeable area) (㎡), within the per capita area Adaptability capacity variables, including gross production (GRDP) (million won), civil servants per population (persons / million), per capita land area (㎡), and the infrastructure vulnerability category for heavy snowfall is the amount of snowfall (cm). Climate exposure variables included, road surface area (㎡) in vulnerable areas of heavy snowfall (1,000 won), sensitivity area including railroad area (㎡), airport area (㎡), gross domestic product per capita (GRDP) (million won) ), Including the adaptive capacity variable including the number of civil servants per population (persons / ten thousand), and the infrastructure vulnerability items for sea level rise, climate exposure variables including the annual rate of increase in seawater temperature, sea level rise vulnerable areas ( Includes road area (㎡), port area (㎡), water pollution prevention facility area (㎡) of soft rising rate (%) Is characterized by including the ability to adapt the parameters, including the sensitivity variables, the number official per per region GDP (GRDP) (millions), population (patients / million), aiding equipment area (㎡).

The present invention relates to a climate change vulnerability assessment system and method thereof.

The present invention conducted in-depth prior inventions and expert consultations, and selected detailed items identified as high necessity of vulnerability assessment in seven sectors.

Here, the detailed items can be said to be priority items for each sector in establishing climate change adaptation measures.

In addition to the selection of sub-categories, sub-substitute variables for climate exposure, sensitivity, and adaptive capacity surrogate variables were selected to evaluate the vulnerability of each sub-item.

Here, climate exposure variables represent the effects of changes in current and future climates, such as daily precipitation and heavy rain days, and sensitivity is a variable representing the degree of impact during climate exposure such as torrential rain and heat waves. Considering the landslide vulnerability due to heavy rain, the slope of the region is equivalent (the higher the slope, the more sensitive to climate exposure), and the capacity to adapt is a socio-economic factor for adaptation to climate change. Possible variables include drainage capacity when assessing vulnerability to rice productivity.

The selected detailed surrogate variables were derived through in-depth preceding inventions and several expert consultations and inventor meetings, which the local governments referred to in selecting surrogate variables for vulnerability assessment.

In particular, the sub-variables that make up the adaptive capacity could be referred to to more clearly identify the areas necessary to reduce vulnerability to specific sectors and specific subcategories.

 For example, areas with high probability of landslides could contribute to lower vulnerability by increasing adaptation capacity such as forest control area and the number of forest officials.

In addition, through the present invention, in order to support the detailed implementation plan for adaptation measures for adaptation to climate change of the local government, climate change vulnerability maps of each sector were produced.

Here, it is possible to compare the relative vulnerability of each country in each sub-category through the vulnerability map, which can help local governments identify sub-items that are considered more vulnerable than other regions in Korea and reflect them in adaptation measures. (However, it is impossible to compare items as absolute value of vulnerability index.)

In addition, climate impact (climate exposure + sensitivity) maps, which are provided along with vulnerability maps, show only the effects predicted from climate change and environmental factors except adaptation ability.

Here, if it is desired to determine the degree of vulnerability only by climate impacts excluding adaptation ability, or if it is difficult to consider adaptation ability, local governments can identify items that are considered necessary for adaptation measures based on climate impact maps.

Based on the vulnerability assessment results, a statistical analysis was conducted by creating a radiation graph.

Here, the radiation graph shows the contribution of surrogate variables reflected in the vulnerability assessment, shows the contribution of climate exposure, sensitivity, and adaptability to vulnerability, and the contribution of sub-substitute to surrogate variables.

In addition, the radiation graph can identify which surrogate variables contribute to the vulnerability of a particular sub-category, and also the contribution of the sub-substitutes to the surrogate variables (e.g. how much annual mean precipitation contributes to climate exposure). Can be.

Therefore, local governments can identify which surrogate is affected by the influence of sub-variables and sub-substitutes, and can refer to them in deciding which surrogate variables to focus on when developing adaptation measures.

The climate change vulnerability assessment tool utilized in the present invention is designed to ensure maximum accessibility of essential basic data for comprehensive and systematic impact assessment and adaptation measures for climate change.

Here, the evaluation tool is aimed at local government policy makers, stakeholders and expert groups, and it is possible to add data and adjust weights to reflect the characteristics of local governments.

In particular, sensitivity and adaptability surrogate variables can be freely modified to take into account the specific characteristics of climate change and regional characteristics.

This enables vulnerability assessments that reflect more detailed and relevant data for each municipality.

In addition, the weight set as the default in the evaluation tool is derived from two expert Delphi surveys, and the weight reflects the opinions of experts in the relevant field.

However, in some cases, when the weight is needed to reflect the characteristics of each region, the weight may be adjusted by using the editing function.

1 is a block diagram showing the configuration of a climate change vulnerability assessment system according to a preferred embodiment of the present invention.
2 is a flow chart showing the flow of climate change vulnerability assessment according to a preferred embodiment of the present invention.
3 is a flow chart showing a standardization process of climate change vulnerability assessment data according to a preferred embodiment of the present invention.
Figure 4 is an exemplary view showing an example of climate change vulnerability assessment map analysis according to a preferred embodiment of the present invention.
5 is an exemplary view showing an example of vulnerability analysis through a radial graph according to a preferred embodiment of the present invention.
6 is an exemplary view showing an example of a vulnerability evaluation result reflecting the future climate of the present invention.
Figure 7 is a picture showing the main screen of the climate change vulnerability assessment tool according to an embodiment of the present invention.
8 is a photograph showing a screen configuration of a climate change vulnerability assessment tool according to an embodiment of the present invention.
9 is a photograph of selecting an evaluation field in the climate change vulnerability assessment tool according to a preferred embodiment of the present invention.
10 is a photograph of the climate change vulnerability assessment item management A screen according to a preferred embodiment of the present invention.
11 is a photograph of the climate change vulnerability evaluation item management B screen according to an embodiment of the present invention.
12 is a photograph for setting a climate change vulnerability surrogate variable weight in accordance with a preferred embodiment of the present invention.
FIG. 13 is a photograph for generating a vulnerability evaluation result drawing which is a vulnerability evaluation result according to a preferred embodiment of the present invention. FIG.
14 is a result of climate change vulnerability assessment according to a preferred embodiment of the present invention.
15 is a photograph showing a climate change vulnerability evaluation radial graph according to a preferred embodiment of the present invention.
16 is a photograph of a method for adding a detailed surrogate variable for climate change according to a preferred embodiment of the present invention.
17 is a photograph confirming the detailed surrogate variable data of climate change vulnerability according to an embodiment of the present invention.

Advantages and features of the present invention, and methods of achieving the same will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. To fully disclose the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout.

Hereinafter, with reference to the accompanying drawings will be described in detail a preferred embodiment of the climate change vulnerability assessment system and method according to the present invention.

1 is a block diagram showing the configuration of a climate change vulnerability assessment system according to a preferred embodiment of the present invention.

Referring to FIG. 1, the climate change vulnerability evaluation system 100 according to the present invention includes a climate exposure variable storage unit 112, a sensitivity variable storage unit 114, an adaptive capacity variable storage unit 116, and an administrative area storage unit ( 118), city and district data conversion unit 120, surrogate variable storage unit 132, weight storage unit 134, surrogate variable standardization unit 142, vulnerability index calculation unit 144, vulnerability index standardization unit 146, The vulnerability standard value comparison unit 148 and the display unit 200 are included.

1. Development of climate change vulnerability assessment methodology

(1) Climate change vulnerability assessment flow

2 is a flow chart showing the flow of climate change vulnerability assessment according to a preferred embodiment of the present invention.

Referring to FIG. 2, the present invention proceeds in a manner that compromises the top-down methodology and the bottom-up methodology for the vulnerability assessment item selected in the prior task “Development of vulnerability standardization methodology”.

The top-down methodology was used to derive climate data, and the data performed prior to this project was used as input data for the bottom-up methodology.

In the bottom-up methodology, the process of constructing data for the production of surrogate variables and selecting surrogate variables to be used in the actual evaluation was carried out. During this process, several expert interviews and numerous document inventions were conducted.

Subsequently, the weight of the surrogate variable was set through the Delphi analysis of the second stage, the surrogate index was standardized, and the vulnerability index was calculated by reflecting the weight in the vulnerability evaluation formula.

Finally, the results of vulnerability assessments were mapped to 232 city, county, and district levels nationwide.

(2) Types of surrogate variables

Substitute variables for assessing vulnerability are classified as climate exposure, sensitivity and adaptability.

Here, the surrogate variable is stored in the surrogate variable storage unit 134, the surrogate variable storage unit 134 is a climate exposure variable which is a variable representing the effect of climate change, a variable representing the degree of influence by the climate exposure variable Phosphorus sensitivity variable and the adaptive capacity variable to reduce the influence of the climate exposure variable and the sensitivity variable, the surrogate variable storage unit, the climate-exposure variable, administrative region generated by the administrative region generated by the municipal data conversion unit 120 It is divided into sensitivity variables and adaptive capacity variables for each administrative district.

In addition, the city-gun data conversion unit 120 refers to the administrative boundary data stored in the administrative storage unit 118, and controls the climate exposure variable for each administrative region through the climate exposure variable stored in the climate exposure variable storage unit 112. And generate a sensitivity variable for each administrative region through the sensitivity variable stored in the sensitivity variable storage unit 114, and generate an adaptive capability variable for each administrative region through the adaptive capability variable stored in the adaptive capability variable storage unit 116. do.

The formula for obtaining variable data (Y (i)) for each administrative district is as follows.

 Where N is the total number of grids. If there are eight grid areas overlapping with administrative area A, the weighted average proportional to the overlapping area of eight data sets is the final data value (concentration) of administrative area A.

If the climate exposure for each grid is not per unit area (e.g. total population, total forest area, etc.), the data per each grid (concentration) is C (i), and the area for each grid overlapping the relevant administrative area is S ( i)

Forecast data by administrative district (Y (i)) =

to be. Where N is the total number of grids, M is the horizontal distance of the grid, L is the vertical distance of the grid, and M × L is the area of the grid. This area allocation method basically converts the grid data of the model into the city and district data. To do this, information about how the area of each grid of the model spans the area within each city and district boundary is provided. Basically needed. As a method for calculating the area occupied by the city and district for each grid area, the following procedure was used.

① Converting city and county boundary data from line information data to area information data

② Convert model grid point data indicated by latitude / longitude to TM-character table

③ Combine grid point data converted to TM coordinates and city and district data converted to area information

④ Calculate the area of overlapping grid points for each city and district

In addition, the climate exposure variable storage unit 112 stores climate exposure variables, which are variables representing the effects of climate change, in the unit of health, forests, ecosystems, agriculture, water management, marine / fishing, and disasters in grid units. do.

In addition, the sensitivity variable storage unit 114, the grid unit by subdividing the sensitivity variable, which is a variable representing the degree of influence of the climate exposure variable by each sector of health, forest, ecosystem, agriculture, water management, marine / fisheries and disasters Save as.

In addition, the adaptive capacity variable storage unit 116 divides the adaptive capacity variable that can reduce the influence of the climate exposure variable and the sensitivity variable for each sector of health, forest, ecosystem, agriculture, water management, ocean / fish and disaster. To store in grid units.

The list of variables according to the detailed items broken down by each sector is shown in Table 1 to Table below.

Tables 1 to 3 below list the variables according to the sub-category broken down into health sectors.

Details Substitute variable Variable list references





By flood
Health Vulnerability
Climate exposure Maximum daily precipitation (mm) Rok Ho Kim (1999),
Kwon Ho Jang (2004),
Yoon-Hyung Park et al. (2006),
Marmot & Wilkinson
(2006),
Vincent (2004) Number of Days with More Than 80mm of Precipitation Flooded area due to flood (ha)


responsiveness Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Percentage of Basic Recipients (%) Lowland area below 10m (km2) Lowland furniture under 10m (family) Flood damage population (person) Number of waterborne diseases (persons)

Adaptability Regional GDP (GRDP) (million won) Financial Independence (%) Percentage of Health Covered Population (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)






Caused by a typhoon
Health Vulnerability
Climate exposure Maximum daily precipitation (mm) Kwon Ho Jang (2004),
Yoon-Hyung Park et al. (2006),
IPCC (2007),
Marmot (2006),
Vincent (2004)
Number of days with 80mm or more precipitation Number of Days with Maximum Wind Speed of 14m / s or More


responsiveness Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Percentage of Basic Recipients (%) Lowland area below 10m (km2) Lowland furniture under 10m (family) Number of waterborne diseases (persons)

Adaptability Regional GDP (GRDP) (million won) Financial Independence (%) Health insurance coverage ratio (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)






Heatwave
Health Vulnerability


Climate exposure Daily maximum temperature (℃) IPCC Fourth Report (2007),
Jongsik Ha (2011) The number of times that the maximum daily temperature is 33 degrees Celsius or more The number of times the day minimum temperature is more than 25 ℃ Feeling temperature (℃) Discomfort Index (-) Thermal wave index (-) Relative Humidity (%)

responsiveness Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Basic Life Recipient Ratio (%) Cardiovascular disease deaths (person) Death toll from heat stroke / heat stroke (persons)

Adaptability Regional GDP (GRDP) (million won) Financial Independence (%) Health insurance coverage ratio (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)

Details Substitute variable Variable list references




Cold
Health vulnerability



Climate exposure
Number of Days with Low Minimum Temperature Jongsik Ha (2009)


The number of times the average daily temperature is below zero Maximum number of consecutive unprecedented days (times) Number of Days with Maximum Wind Speed of 14m / s or More Snowfall (cm)

responsiveness
Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Percentage of Basic Recipients (%) The number of patients admitted for respiratory disease (person) Cerebrovascular disease death toll (person)

Adaptability
Regional GDP (GRDP) (million won) Financial Independence (%) Percentage of Health Covered Population (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)





Ozone concentration
Due to rise
Health Vulnerability







Climate exposure Daily maximum temperature (℃)



Soo-Heon Cho (1999),
Kwon Ho Jang (1998),
Sunyoung Kim (2004),
Sunyoung Kim (2007)





On days when the ozone concentration is more than 100 ppb
Number of times (times) Days of Ozone Advisory Notice (Days) The number of times 8 days cumulative ozone concentration is 60ppb / 8hr or more


responsiveness Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Percentage of Basic Recipients (%) Number of inpatients for respiratory disease (persons) Cardiovascular disease deaths (person)

Adaptability
Regional GDP (GRDP) (million won) Financial Independence (%) Health insurance coverage ratio (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)




Fine dust
by
Health Vulnerability




Climate exposure Daily maximum temperature (℃)




Young Ok Park (2002),
OkHee Yi (2010)




Fine dust concentration (ug / ㎥) The number of times the dust concentration is 100ug / ㎥ or more (times)

responsiveness Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Percentage of Basic Recipients (%) The number of patients admitted for respiratory disease (person) Cardiovascular disease deaths (person)

Adaptability
Regional GDP (GRDP) (million won) Financial Independence (%) Health insurance coverage ratio (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)

Details Substitute variable Variable list references





Etc
Air pollution
By substance
Health Vulnerability



Climate exposure Daily maximum temperature (℃) Ren, C. Tong (2006),
Qian, Z. He (2008),
Katsouyanni (1993),
Ae Kyung Park (2009)
CO (Outside Residential Heating) (kg) Industrial emissions (CO) (kg) SOx (Emissions from Residential Heating) (kg) SOx (industrial emissions) (kg) NOx (Emissions out of residential heating) (kg) NOx (industrial emissions) (kg)

responsiveness Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Percentage of Basic Recipients (%) The number of patients admitted for respiratory disease (person) Cardiovascular disease deaths (person)

Adaptability Regional GDP (GRDP) (million won) Financial Independence (%) Health insurance coverage ratio (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)





Insects and
On rodents
Infectious diseases
Health Vulnerability




Climate exposure The number of times the day maximum temperature is more than 33 ℃ Yoon-Hyung Park et al. (2006),
Signaling et al. (2008),
Marmot & Wilkinson
(2006),
UNDP (2005),
WHO (2004)






The number of times the day minimum temperature is more than 25 ℃ Maximum daily precipitation (mm) Number of Days with> 80mm of Precipitation

responsiveness Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Percentage of Basic Recipients (%) Average number of malaria cases per year The average number of people with Tsutsugamushi disease per year (person)

Adaptability Regional GDP (GRDP) (million won) Financial Independence (%) Health insurance coverage ratio (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)



Waterborne parameters
For the disease
Health Vulnerability



Climate exposure
The number of times that the maximum daily temperature is 33 ℃ or more (times)


Yoon-Hyung Park et al. (2006),
Signaling et al. (2008),






The number of times the day minimum temperature is more than 25 ℃ (times) Maximum daily precipitation (mm) Number of Days with More Than 80mm of Precipitation
responsiveness
Population age 65 or older Population 13 years or younger Elderly living alone (total population) (%) Percentage of Basic Recipients (%)

Adaptability
Number of waterborne diseases (persons) Regional GDP (GRDP) (million won) Financial Independence (%) Health insurance coverage ratio (%) Public health center workforce per person (person / 10,000) Number of emergency medical institutions per population (unit / million) GRDP Health and Social Services (Million KRW)

Tables 4 to 5 below list variables according to the detailed items that are divided into forest sectors.

Details Item Substitute variable Variable list references



In heavy rain
Landslide
Climate exposure The number of times (day) when the daily precipitation is more than 80 mm Park Jong Min et al. (2010),
Hong Won-Pyo (1990),
Oliver (1994)
Maximum daily precipitation (mm) June-August Precipitation (mm) Maximum amount of precipitation for 5 days (mm)
responsiveness Average slope in the forest (degrees) Conifer area (ha) Average altitude in the forest (m) Free land area (ha)
Adaptability Forest officials (persons) Forest Control Area (ha) Regional GDP (GRDP) (million won) Financial Independence (%)


In a landslide
By forest road
Vulnerability
Climate exposure The number of times (day) when the daily precipitation is more than 80 mm Park Jong Min et al. (2010),
Hong Won-Pyo (1990),
Oliver (1994) Maximum daily precipitation (mm) June-August Precipitation (mm) Maximum amount of precipitation for 5 days (mm)

responsiveness Average slope in the forest (degrees) Conifer area (ha) Average altitude in the forest (m) Free land area (ha) Distance of forest road (m)
Adaptability Forest officials (persons) Forest Control Area (ha) Regional GDP (GRDP) (million won) Financial Independence (%)




Against forest fires
Vulnerability
Climate exposure The number of times that the maximum daily temperature is 33 ℃ or more (times) Lee Si Young et al. (2002),
Won Myung Su et al (2010),
Lee Byung Doo et al. (2008),
Kwak Han-bing (2010) Number of Days with Maximum Wind Speed of 14m / s or More The number of times the effective humidity is less than 35% (times) Maximum number of consecutive unprecedented days (times)

responsiveness Conifer area (ha) Hardwood area (ha) Mixed forest area (ha) Soil moisture 10cm (m³ / m³) Average slope in forest (degrees) Total population (persons)
Adaptability Forest officials (persons) Forest Control Area (ha) Regional GDP (GRDP) (million won) Financial Independence (%)


On pests
by
Pine
Vulnerability
Climate exposure Precipitation (mm) of from June to August (summer season) Son Min-ho et al. (2006),
Joon Bum Kim (2003) Daily minimum temperature (℃) of from June to August (summer season) Daily high temperature (℃) from June to August (summer season) Number of Days with Maximum Wind Speed of 14m / s or More
responsiveness Area of pine wood (m²) Average slope in forest (degrees) Average altitude in the forest (m) Infestation area (ha)

Adaptability Forest officials (persons) Pest Control Employees (persons) Forest Control Area (ha) Regional GDP (GRDP) (million won) Financial Independence (%)

Details Substitute variable Variable list references



Pine tree
Matsutake
Vulnerability

Climate exposure June-August Precipitation (mm) Park Yong Hoon (2010),
Kang, An-Seok and others (1988)
June-August Daily Average Temperature (℃) Precipitation (mm / day) Soil moisture 10cm (m³ / m³) Maximum number of consecutive unprecedented days (times)
responsiveness Area of pine wood (m²) Average altitude in the forest (m) Forest byproducts production (ton)
Adaptability Forest officials (persons) Forest Control Area (ha) Regional GDP (GRDP) (million won) Financial Independence (%)



Forest
Productivity
Vulnerability

Climate exposure Precipitation (mm / day) Jong Hwan Lim
(2006),
Korea Environment Corporation
(2010)




Highest temperature (℃) Lowest temperature (℃) Maximum number of consecutive unprecedented days (times)
responsiveness Conifer area (ha) Hardwood area (ha) Mixed forest area (ha)

Adaptability Forest officials (persons) Forest Control Area (ha) Area of natural rest year (m²) Natural Forest Nursery (ha) Financial Independence (%) Regional GDP (GRDP) (million won)



Drought
Forest vegetation
Vulnerability


Climate exposure Precipitation (mm / day) Jong Hwan Lim
(2006),
Korea Environment Corporation
(2010)



Maximum number of consecutive unprecedented days (times) The number of times the effective humidity is less than 35% (times)
responsiveness Conifer area (ha) Hardwood area (ha) Mixed forest area (ha) Plantation area (ha)

Adaptability Forest officials (persons) Forest Control Area (ha) Regional GDP (GRDP) (million won) Financial Independence (%) Natural Forest Nursery (ha)

Tables 6 to 7 below list variables according to the detailed items organized by subdividing the ecosystem sector.

Details Substitute variable Variable list references Coniferous
Vulnerability
(Tree Growth Division
Distribution
Vulnerability)


Climate exposure
January-March Daily Average Temperature (℃) Woo Seok Gong (1995, 1996, 2004),
Boa
(2001a),
Sungju
(2001),
National Forest Research Institute (2009),
This state
(2009),
A classmate
(2010),
Allen et al.
(2010) Temperature (° C) Precipitation (mm / day) June-August Daily Average Temperature (° C) June ~ August Highest temperature (° C)


responsiveness Conifer distribution area (ha) Softwood Wood Production (㎥) Production of Coniferous Forest By-Products (㎥) Forest-related working population (person) Tree felling area (ha) Agriculture and forestry business number () The number of agriculture and forestry workers (person)
Adaptability Conifer planting area (ha) Natural Forest Nursery (ha) Forest officials (persons)






Insect
Vulnerability





Climate exposure January to March average temperature (° C) Boredom
(2002, 2008).
Park Young Seok and others
(2003),
Jong Hwan Lim
(2008),
Park Gyu Taek and others
(2006),
Han, Ju-hwan etc.
(2009),
Bang Hye Sun
(2008) The number of times the daily average temperature is below zero Average daily temperature in April (° C) Average Relative Humidity in April (%) Maximum number of consecutive unprecedented days (times) Amount of evapotranspiration (mm / day) Insolation (W / m2) June-August Daily Average Temperature (° C)


responsiveness Forest pests Forest pathogen-Fusarium blight disease (the number) Bee farmers Bee breeding scale (bucket) Insect-borne infectious disease number of people (person) Pest damage logging area (ha) Infestation Damage Damaged (㎥)

Adaptability Pest Control Period-Little Pear Tree Forest Control Area (ha) Pest Control Employees (persons) Number of bioindustry companies Eco-Friendly Fruit Trees (Furniture)

Details Substitute variable Variable list references







National park
Vulnerability
(Preservation area
Management
Vulnerability)



Climate exposure Precipitation (mm / day) Ministry of Environment
(http://www.me.go.kr) Maximum number of consecutive unprecedented days (times) The number of times the daily average temperature is below zero The number of times that the maximum daily temperature is 33 ℃ or more (times) Precipitation in March-May (mm / day) Precipitation in June to August (mm / day) Precipitation in September to November (mm / day) Precipitation in December-February (mm / day) Number of Days with More Than 80mm of Precipitation Number of Days with Maximum Wind Speed of 14m / s or More


responsiveness Number of plant species in the national park Number of animal species in the national park Visitors (persons) Visitor YoY Growth (%) Plant endangered species (species) Endangered Species of Species (species) Should be linked to national park management
Number of administrative divisions


Adaptability National park staff (person) National park organization number () National Park Offices National park area increase and decrease National park commentary operation frequency National Park Temple Area Area of natural rest year (㎡) Natural rest year enforcement distance (km)

Tables 8 to 9 below list the variables according to the detailed items organized by subdividing the agricultural sector.

Details Substitute variable Variable list references



Farmland
Soil erosion
Vulnerability
Climate exposure Precipitation (mm / day) New Territories (1999),
Heterologous
(2007),
Kim Joo-hoon (2009),
Pil Kyun Chung (1983),
Wischmeier and Smith (1978),
A lantern
(2004, 2005),
Wischmeier
(1971),
Park Jae-dong (2000),
National Statistics Portal The number of times (day) when day precipitation is more than 80mm Precipitation days (10 mm or more) (times)
responsiveness Open field area (ha) Field area (ha) Regional mean slope (deg)

Adaptability Number of farm machinery held per area of cropland (log / ha) The number of the farm households with the PC utilization / total farms Agricultural population per square meter (ha / ha) Staff working on maintenance of arable land (person / ha) Gross Domestic Product (GRDP) per Capita (KRW Million) Financial Independence (%)



Cultivation / Breeding
Of facility collapse
Vulnerability
Climate exposure Number of Days with Maximum Wind Speed of 14m / s or More Meteorological Agency,
National disaster
Information Center,
National Statistics Portal The number of times the snowfall more than 20cm (times) The number of times (day) with more than 160mm of precipitation
responsiveness Facility crop cultivation area (ha) Breeding area (m²) House damage area per plant crop area Number of livestock livestock damage per area of breeding facilities (point / ha)

Adaptability Agricultural population per person / cultivation area (person / ha) The number of the farm households with the PC utilization / total farms Staff working on maintenance of arable land (person / ha) Gross Domestic Product (GRDP) per Capita (KRW Million) Financial Independence (%)

Details Substitute variable Variable list references







Of rice productivity
Vulnerability





Climate exposure
The number of times (time) of April-June minimum temperature less than 13 degrees Celsius
National Institute of Crop Science (2009),
Nakamura and Saka
(1978),
National Institute of Crop Science
National disaster
Information Center The number of times (the times) that day minimum temperature is 17 degrees Celsius or less The number of times of the day when the minimum temperature is 14 degrees Celsius or less The number of times (the number of times that the highest temperature temperature is 30 degrees Celsius or more) from April to October The number of times (time) that time ozone concentration is more than 100ppb from April to October Log (April to October solar radiation sum) The number of times (day) with more than 160mm of precipitation Number of Days with Maximum Wind Speed of 14m / s or More
responsiveness Paddy area (ha) Crop damage area per area Pest Damage Potential


Adaptability Rice production per cultivated area (ton / ha) Number of rice seedlings per planted area (person / ha) Arrangement rate for arable land (%) The number of the farm households with the PC utilization / total farms Employees in maintenance business of arable land (person) Gross Domestic Product (GRDP) per Capita (KRW Million) Financial Independence (%)




Apple
Productivity
Vulnerability




Climate exposure Annual average temperature range 8 ℃ ~ 11 ℃ Kim Jung Ho
(1998),
Hyung-Ho Seo (2004),
National Horticulture
Special Science Institute
(2010),
National disaster
Information Center,
National Statistics Portal,
Seo Hyung-Ho
Park Heeseung (2003),
Eun Young Kwon (2004) October average temperature (℃) Daily average temperature (℃) from April to August August average temperature (℃) April to August Highest temperature (℃) April-October Precipitation (mm) Number of Days with Maximum Wind Speed of 14m / s or More in April ~ October responsiveness
Apple growing area (ha) Crop damage area per crop

Adaptability Apple yield per planted area (kg / 10a) Number of main seedlings per planted area (person / ha) Number of agricultural machinery per apple cultivation area (large / ha) PC-powered farmer / apple farmer Employees in maintenance business of arable land (person) Gross Domestic Product (GRDP) per Capita (KRW Million) Financial Independence (%)



Livestock
Productivity
Vulnerability

Climate exposure The number of times that the maximum daily temperature is 27 ℃ or more (times) Meteorological Agency,
National Institute of Animal Science (2010),
National Statistics Portal,
Ministry of Environment
National Institute of Environmental Science
(2004),
Swine Times
(2011),
National Disaster Information Center The number of times when the temperature and humidity index is 72 or more The number of times the snowfall more than 20cm (times) Number of Days with Maximum Wind Speed of 14m / s or More
responsiveness Livestock breeding head (thousands) Number of livestock livestock damage per area of breeding facilities (point / ha) Livestock Disease Risk

Adaptability Number of livestock workers per breeder area (person / ha) Livestock Wastewater Treatment The number of PC utilization farmhouse / livestock farmhouse Staff working on maintenance of arable land (person / ha) Gross Domestic Product (GRDP) per Capita (KRW Million) Financial Independence (%)

Tables 10 to 11 below are lists of variables according to the detailed items organized by subdividing the water management section.

Details Substitute variable Variable list references







Dimension
Vulnerability

Climate exposure Maximum daily precipitation (mm) Eun Eun Jung
Gil Sung Lee (2007)
Jung Il Won, etc.
(2008)
Jaekyung Ko (2009)
Myung Soo Jung (2011)
Minwoo Son
(2011)
Jung et al
(2011)
Jun et al
(2011) Number of Days with More Than 80mm of Precipitation Maximum rainfall in 5 days (mm) Ground runoff (mm / day) June-September Precipitation (mm)


responsiveness Less than 10 m low area (km) Low land furniture (household) less than 10m Dike Use Area Ratio (%) Population Density (persons / km) Total population Regional mean slope (deg) Road area ratio (%) Flood damage in the last three years (thousand won) Flood victims in recent 3 years


Adaptability Financial Independence (%) The number of civil servants per population (person / ten thousand) Regional Gross Domestic Product (GRDP) (million won) Water management officials per area (person / km2) River yield (%) Domestic drainage facility drainage capacity (m / min) Reservoir flood control capacity (million meters)







Isu
Vulnerability

Climate exposure Maximum number of consecutive unprecedented days (days) Eun Sung Jung and Gil Sung Lee (2007)
Jung Il-won et al. (2008)
Jaekyung Ko (2009)
Myung Soo Jung (2011)
Minwoo Son et al. (2011)
Jung et al (2011)
Jun et al (2011)



December-February Precipitation (mm) Precipitation in March-May (mm) December ~ February evapotranspiration (mm) March-May evapotranspiration (mm) Underground runoff (mm / day)



responsiveness Population Density (persons / km) Total population (persons) Water supply: 1 liter of water Grain production per area (ton / km) Livestock Production per Area (Marie / km) Groundwater capacity (m / year) River water capacity (m / year) Water for living (1000m / year) Industrial water use (1000m / year) Agricultural water use (1000m / year)


Adaptability Financial Independence (%) The number of civil servants per population (person / ten thousand) Regional Gross Domestic Product (GRDP) (million won) Number of Water Management Officials per Area Water supply rate (%) Groundwater capacity (1,000㎥ / year) Reservoir for water supply per unit area Low capacity (1000m) Water consumption per unit area (1000m / year)

Details Substitute variable Variable list references





Water quality and
Aquatic
About
Vulnerability

Climate exposure Daily high temperature (℃ Yu Ga-young and others
(2008),
W Neil Adger et al. (2004),
Jaekyung Ko (2009)
Maximum daily precipitation (mm) Number of Days with More Than 80mm of Precipitation Maximum number of consecutive zero precipitation days The number of times the maximum daily temperature is 33 ℃ or more (times) The number of times the day minimum temperature is more than 25 ℃ (times)


responsiveness Regional mean slope (deg) River yield (%) Livestock industry population (persons / ten thousand) Livestock Production Status (Marie / km2) Fertilizer usage per arable land (ton / ha) Major species distribution (species) Major Plant Species Distribution Forest area rate (%) Managed Land Rate (%)

Adaptability Sewage spread rate (%) Number of civil servants per population (person / ten thousand) Population Density (%) Road area ratio (%) Road length per unit area (km)

Table 12 below lists the variables according to the sub-categories of the fishery sector.

Details Substitute variable Variable list references




Over temperature change
According
Fisheries
(Aquaculture)
Vulnerability


Climate exposure Sea surface temperature (℃) Kim Do-hoon (2010), Kim Ja-myung
(2011), Pukyong National University (2010),
Yoon Jin-hee et al. (2007),
Yongkyu Choi (2010),
Han et al. (2011),
Turner et al. (2003) Annual Seawater Temperature Rise (℃ / yr) The number of times the daily average temperature is below zero The number of times the maximum daily temperature is 33 ℃ or more (times) Number of Days with More Than 80mm of Precipitation Number of jellyfish damages (times)

responsiveness Areas for aquaculture farming facilities (fish-sea cages) (ha) Aquaculture Breeding Area (Business-Marine Cage) (ha) Aquaculture facility area (fishing festival) (ha) Aquaculture Breeding Area (Business-Festival) (ha) Western fish farming situation (sea cage) (point) Farmed Fish Status (Festivals) (Location)

Adaptability Financial Independence (%) Number of civil servants per population (person / ten thousand) Aquaculture Breeding Area (Fish-Land Tank) (ha) Areas for aquaculture farming facilities (businesses-land tanking) (ha) The style fish aquaculture situation (land tank) (point)

Table 13 below is a list of variables according to the sub-category broken down into disaster (infrastructure) sectors.

Details Substitute variable Variable list references


About flood
Infrastructure
Vulnerability Climate exposure The number of times (day) with more than 80mm / day of precipitation Jaekyung Ko (2009),
Lee Dong-keun (2011) Maximum daily precipitation (mm)
Min
feeling
Degree flood
weak
area
(Flooding
building
Number)
(copper) Road area (㎡) Water supply facility area (㎡) Area of electricity supply facility (㎡) Area of gas supply facility (㎡) Area of heat supply facility (㎡) Oil storage and oil supply area (㎡) Sewage area (㎡) Water pollution prevention facility area (㎡)
Adaptability Gross domestic product per capita (GRDP) (KRW million) Number of civil servants per population (person / ten thousand) River yield (%)



Against heatwave
Infrastructure
Vulnerability Climate exposure The number of times that the maximum daily temperature is 33 ℃ or more (times) Yu Ga-young et al. (2008)
Jaekyung Ko (2009)




The number of times the day minimum temperature is more than 25 ℃ (times) Min
feeling
Degree Heat wave
weak
area
(Unspecific
Number
area
area)
(㎡)


Road area (㎡)
Adaptability Gross domestic product per capita (GRDP) (KRW million) Number of civil servants per population (person / ten thousand) Green area per person (㎡)


For heavy snow
Infrastructure
Vulnerability
Climate exposure Snowfall (cm) National environment
Science
(2010)






Min
feeling
Degree heavy snow
weak
area
(heavy snow
damage
liquid)
(One thousand won) Road area (㎡) Railway area (㎡) Airport area Adaptability Gross domestic product per capita (GRDP) (KRW million) Number of civil servants per population (person / ten thousand)




Sea level
About rising
Infrastructure
Vulnerability Climate exposure Coastal Water Temperature Rise (%)
Jaekyung Ko (2009),
USGS Development
CVI Calculation Criteria,
IPCC Standard Methodology
(1990)



Min
feeling
Degree

Sea level
Increase
weak
area
(year
condolence
% Increase)

Road area (㎡) Port area (㎡) Water pollution prevention area (㎡)
Adaptability Gross domestic product per capita (GRDP) (KRW million) Number of civil servants per population (person / ten thousand) Assistance area (㎡)

(3) Substitute variable setting

Each of the elements that make up the surrogate variables for assessing vulnerabilities is shown in Table 14 below.

Substitute variable Justice Climate exposure Variables representing climate change impacts (generally climate factors) responsiveness Variables indicating the extent of climate exposure impacts (socio / economic statistics) Adaptability Variables that can reduce the impact of climate change (socio / economic statistics)

Vulnerability was derived by setting surrogate variables including exposure variables, sensitivity variables, and adaptability, and selecting detailed surrogate variables corresponding to climate exposure variables, sensitivity variables, and adaptability variables.

In addition, in order to set detailed surrogate variables of climate exposure variables, sensitivity variables, and adaptability variables, consultations were conducted through a number of expert meetings based on the literature invention in the prior task, 'Development of Vulnerability Assessment Standardization Methodology'.

(4) Data standardization method

3 is a flow chart showing a standardization process of climate change vulnerability assessment data according to a preferred embodiment of the present invention.

Referring to FIG. 3, the present invention standardizes surrogate variables, calculates a vulnerability index, standardizes a vulnerability index, and compares the results of vulnerability evaluation among national governments through a standardized vulnerability index.

In order to introduce and calculate the actual values of substitutive variables such as climate exposure, sensitivity, and adaptability into vulnerability assessment formulas, a method of standardizing various values is required.

In the present invention, by using Equation 1 below, the various surrogate variables are normalized to values having a range of 0 to 1.

-In the standardization process, the standardization of substitutive variables, the standardization of surrogate variables (climate exposure, sensitivity, and adaptability), and finally the standardization of vulnerability index are needed.

-In case of standardization of substitutive variables, the values constituting the variables were standardized from 1 to 0 and introduced into the vulnerability evaluation formula by substituting the weight corresponding to the subvariables.

In addition, the standardization unit 142 of the surrogate variable standardizes the surrogate variable value including the climate exposure variable, the sensitivity variable, and the adaptive capacity variable (up to 1 to 0) to derive the surrogate variable standardization value and substitute the surrogate variable. The vulnerability index was calculated by applying the weight stored in the weight storage unit 134 to the variable normalization value.

Here, the weights are weighted for climate exposure variables, sensitivity variables, adaptability variables and detailed surrogate variables within each surrogate variable to assess local vulnerability in each sector of health, forest, ecosystem, agriculture, water management, marine / fish and disaster. It is to calculate the vulnerability index by applying a weight to the, the weight is stored in the weight storage unit 134.

Here, the weight storage unit 134 derives and applies weights for surrogate variables of climate exposure, sensitivity, adaptability, and substitutive variables in each category through Delphi survey.

In the case of vulnerability evaluation data derived through the above process, it is difficult to compare even within each subcategory, so the standardization process of maximum 1 and minimum 0 was introduced for all scenario values.

The vulnerability index was also defined as the difference between climate impacts and adaptability, which consist of climate exposure and sensitivity.

In the present invention, the vulnerability index was calculated using Equation 2 below.

(α, β, γ means weight)

Through the Delphi survey, the weights of surrogate variables of climate exposure, sensitivity and adaptability, and the weights of substitutive variables in each category were derived, and the vulnerability index was calculated by applying the derived weights.

In addition, the calculated vulnerability index is once again standardized to derive the vulnerability index standardization value, and the vulnerability index standardization unit 146 serves to derive it.

Herein, the vulnerability index standardization unit normalizes the vulnerability indexes to values having a range of 0 to 1 using Equation 3 below.

In addition, the standardized vulnerability index standardized value is compared by administrative region in the vulnerability standardized value comparison unit 148.

In addition, the administrative region geographic information is displayed on the display unit from the administrative boundary boundary data stored in the administrative region storage unit 118, climate exposure variables for each administrative region, sensitivity variables for each administrative region, The adaptive capability variable or vulnerability index and vulnerability index standardization value for each administrative region are displayed on the display unit 200 together with the administrative region geographic information.

(5) Delphi analysis

A total of 10 to 12 experts were drawn up in each of the seven sectors, and a Delphi survey was conducted for the experts.

A total of two Delphi surveys were conducted and the sectoral climate change vulnerability was derived by reflecting the final Delphi survey results.

To assess the vulnerability of local governments in seven sectors, an expert Delphi survey was conducted to find the weights for climate exposure, sensitivity, adaptability, and the weights for the detailed surrogate variables within each surrogate variable.

An example of the Delphi questionnaire in the present invention can be summarized in Table 15 below as an example of the disaster field, one of seven fields.

Details Substitute variable Weighted sum Weight (Delphi Second Survey)
flood
Climate exposure
One
0.42 responsiveness 0.29 Adaptability 0.29
Heat wave
Climate exposure
One
0.43 responsiveness 0.19 Adaptability 0.38
heavy snow
Climate exposure
One
0.40 responsiveness 0.33 Adaptability 0.27
sea level rise
Climate exposure
One
0.43 responsiveness 0.26 Adaptability 0.31

First, when the details of the disaster area were determined, the weights of climate exposure, sensitivity, and adaptability were queried through the Delphi questionnaire.

In addition, researchers in each field selected a list of surrogate variables in the subcategory to prepare a Delphi survey.

In addition, Delphi survey was conducted to experts in each field to derive weights for surrogate variables and substitutive variables.

Based on the primary results generated through this, the second Delphi was implemented, and the weights derived from the second were inputted into CCGIS to conduct the sectoral vulnerability assessment of the local government.

Therefore, in all seven fields, the same method was used to derive sub-items and sub-variables for each item, and to obtain weight values through Delphi.

This method, compared with the other Delphi methods, obtained a weight in two steps naturally resulted in cascading the surrogate variable.

2. How to use climate change vulnerability assessment map

(1) Composition of vulnerability map by climate change sector

Climate change vulnerability assessment provides climate change vulnerability in local governments by presenting surrogate variables, vulnerability assessment maps, and statistical analysis by state and province for health, forestry, water management, ecosystem, agriculture, ocean / fishing and disasters. Based on this, it was used to establish adaptation measures.

Table 16 below shows the contents of the sectoral climate change vulnerability assessment.

Details Composition 1. Substitute table Substitution Variable List and Weight


2. Lead





1) current


Climate exposure responsiveness Climate impact Adaptability Vulnerability Statistical analysis of unit of city and district 2) future
Vulnerability Climate impact 3. Attempt unit statistical analysis
Attempt vulnerability group analysis Contribution Analysis

One sub-item consists of a large variable table, a map, and a statistical analysis for each city and province. In the first part, the Substitution Variables Table, a list of substitutive subvariables, weights, selection criteria, and references in each sector was compiled.

4 is an exemplary view showing an example of climate change vulnerability assessment map analysis according to a preferred embodiment of the present invention, Figure 5 is an exemplary view showing an example of vulnerability analysis through a radial graph according to a preferred embodiment of the present invention.

Referring to FIGS. 4 and 5, the second part, the map part, was prepared by dividing it into the present and the future, and consists of current climate exposure, sensitivity, climate impact, adaptability, vulnerability, and statistical analysis by city, county, and district. The results are summarized based on current vulnerability.

Maps of climate exposure, sensitivity, climate impacts, adaptability, and vulnerability are compiled based on the current point in time (2000s).

Statistical analysis by city, county, and district consists of contribution analysis that identifies the contribution of surrogate variables to vulnerabilities and also the contribution of each detailed surrogate variable that constitutes surrogate variables.

The contribution of surrogate variables affecting the vulnerability of 232 cities, counties, and districts across the country was explained, and the contributions of detailed surrogate variables that constitute climate exposure, sensitivity, and adaptability were analyzed.

In fact, the results of the analysis of which surrogate variables have the greatest impact on vulnerability to climate change by sector and which sectors should be emphasized in establishing adaptation measures in local governments were derived.

6 is an exemplary view showing an example of a vulnerability evaluation result reflecting the future climate of the present invention.

Referring to FIG. 6, the future is summarized so that the vulnerability and climate impact maps of the 2020s, 2050s, and 2100s can be viewed at a glance.

In addition, legends of vulnerability and climate impact maps can be time-series analyzed by unifying their values.

(2) Vulnerability Map Application Plan

Climate change vulnerability assessment, using the climate change vulnerability assessment tool, assessed regional and sectoral climate change vulnerability.

Here, it is possible to grasp the items necessary for the vulnerability assessment from the derived subcategories and surrogate variables, which can be used to select subcategories for establishing the local government's adaptation plan for climate change.

Climate exposure values by subcategory can be used to identify the national distribution of climate impacts affecting vulnerability to future climate change, and a list of climate exposure variables can be used to identify which factors have a major impact on the municipality. Can be.

Sensitivity values by subcategory can be used to identify vulnerabilities in the environmental aspect except climate factors as a national distribution, and the list of sensitivity variables can be used to identify environmental factors that increase vulnerabilities along with climate factors.

The adaptive capacity value by subcategory can identify the adaptability of the local government to the vulnerability, and the list of adaptive capacity variables can identify the factors necessary to reduce the vulnerability, which can be used for establishing climate change adaptation measures.

(3) Composition of climate change vulnerability assessment tool

The Climate Change Adaptation Toolkit based on GIS (CCGIS) is designed to ensure maximum access to essential basic data for comprehensive and systematic impact assessment and adaptation measures for climate change.

It was programmed to calculate the vulnerability index by calculating the climate change vulnerability indicators necessary for establishing climate change adaptation measures by 232 cities, counties, districts, and seven vulnerability evaluation sectors using the climate change vulnerability assessment tool.

In addition, it is designed to express calculated indicators by region based on GIS.

The 2020, 2050, and 2100 future scenario data built into this vulnerability assessment tool were produced for all six IPCC climate change scenarios (SRES A2, A1B, A1T, A1FI, B1, B2). Each climate forecast data is downscaled from the results of the Community Climate System Model version3 (CCSM3), a “bulb climate model” of the National Institute of Environmental Research, and inputs data from the Regional Climate Model (RCM). This is the final production data.

This data was produced using the optimized options for each model run as climate prediction data for the Korean Peninsula, but the uncertainty in the model results from the predictions is inherent.

(4) Application of evaluation tools

To explain briefly how local government policy makers, stakeholders, and the general public can use the evaluation tool, which is expected to use the vulnerability assessment tool. There is a function included, and the weight adjustment to reflect the characteristics of each municipality is also possible.

7 is a picture showing a main screen of the climate change vulnerability evaluation tool according to a preferred embodiment of the present invention, Figure 8 is a picture showing a screen configuration of the climate change vulnerability evaluation tool according to a preferred embodiment of the present invention, Figure 9 Is a photograph of selecting an evaluation field in a climate change vulnerability assessment tool according to a preferred embodiment of the present invention.

-Vulnerability assessment begins with a selection of seven areas.

-Select the field to perform vulnerability assessment from the field selection on the top left home menu.

-In the example, when assessing the vulnerability of landslides caused by heavy rain, one of the details of the forest sector,

First, click on the forest area to create a window to begin vulnerability assessment.

10 is a photograph of the climate change vulnerability assessment item management A screen according to a preferred embodiment of the present invention.

Referring to FIG. 10, when a vulnerability assessment item management is clicked in the tool menu at the top, a window called 'forest: climate change vulnerability assessment item (VRI) management' (FIG. 11) appears, and a desired vulnerability assessment detail can be edited or added. Can be.

12 is a photograph for setting a climate change vulnerability surrogate variable weight in accordance with a preferred embodiment of the present invention.

 Referring to FIG. 12, in this step, the detailed substitute variable and the weight of the substitute variable may be input.

 In the example, we edited the landslide vulnerability.

If you press the add button, the window as below will be created.

The generated window consists of surrogate variables for climate exposure index, climate change sensitivity, and adaptability, and in detail contains more diverse surrogate variables.

The weights listed in [1] are the weights of the substitutive variables, and the weights in [2] are the weights for climate exposure, sensitivity, and adaptability. In the blank [3] under the Climate Change Vulnerability-Resilience Index (VRI), enter the title of the sub-item you are currently evaluating (in this example, 1. Landslide vulnerability).

In the existing set weight, the inventor's invention result is input as a default value.

It is possible to input weights and change variables reflecting the characteristics of local governments, and if changes are needed, they can be reset and evaluated by local governments.

In this case, when the confirmation is pressed, a vulnerability evaluation expression for one subitem is input and the configuration of the expression is completed.

When the vulnerability evaluation item management window is closed, a vulnerability evaluation result drawing (FIG. 13) that is a vulnerability evaluation result is generated.

14 is a result of climate change vulnerability assessment according to a preferred embodiment of the present invention.

To check this, if you look at the tool in the menu, 'Vulnerability Index View' is selected by default. View Standardized Index is a menu that adjusts the legend based on the vulnerability index values of the present (2000s), 2020s, 2050s, and 2100s.

In the menu, you can set the scenarios such as the past basis, A2, A1B, B1, etc. in the menu, and you can set the period of the present (2000s), 2020s, 2050s, 2100s, etc. in the slice.

'Climate Exposure, Climate Sensitivity, Adaptability, Climate Impact View' is a menu that displays each item as it is expressed. You can edit the color of the legend by clicking the color on the right.

15 is a photograph showing a climate change vulnerability evaluation radial graph according to a preferred embodiment of the present invention.

Referring to FIG. 15, the vulnerability assessment result may be generated as a radial graph.

To create the radial graph covered in the report, you need to open the Radial Graph Generation window by clicking on the tool 'View by Administrative Region (Radial)'.

In the upper left corner of the radial graph creation window, there are menus for 'Detailed Item Selection', 'Administrative Area Setting', and Administrative Area Item Management '. In particular, administrative area item management has the function to compare various administrative areas. In addition, there are menus for setting the style such as font size and color of the graph.

16 is a photograph of a method for adding a detailed surrogate variable for climate change according to a preferred embodiment of the present invention.

Referring to FIG. 16, the present invention may add a new detailed surrogate variable.

If you want to add sub-substitutes other than the existing sub-substitutes, you can use the tool's User Climate Factor Management.

-Click on 'User Climate Factor Management' and press 'Add' to create the 'User Climate Factor Information Input' window. After arranging the data in the order of 232 cities, counties, and districts organized in the program, the data is organized in an Excel file. After copying 232 values, select the first column of the program's `` User climate factor information input window '' and paste it. You can add new material.

-By modifying the 'name' of the data attribute, you can set the name of the input user climate factor information.

17 is a photograph confirming the detailed surrogate variable data of climate change vulnerability according to an embodiment of the present invention.

Referring to Figure 17, it is newly created in the 'user climate / air environment'.

Each municipality can use this function to add more detailed data held by local governments to the evaluation tool and reflect them in the vulnerability assessment.

3. conclusion

(1) Major Invention Results and Expected Effects

In the present invention, in-depth prior invention and expert consultation were conducted on the detailed items derived from the “Vulnerability Assessment Standardization Methodology” task, which was previously conducted, and selected detailed items identified as high necessity of vulnerability assessment in seven sectors. .

 The detailed items derived through this process can be said to be the priority items for each sector in establishing the adaptation plan for climate change.

In addition to the selection of subcategories, sub-substitute variables for climate exposure, sensitivity, and adaptive capacity surrogate variables were selected to assess vulnerability in each subcategory.

Climate exposure variables represent the effects of changes in current and future climate, such as daily precipitation and heavy rain. Sensitivity is a variable that indicates the degree of impact from climate exposure, such as torrential rain and heat waves, and the slope of the area corresponds to the landslide vulnerability (the higher the slope, the more sensitive to climate exposure). Adaptation capacity is a socio-economic factor for adaptation to climate change, which means a variable that can reduce the impact of climate change, and drainage capacity when assessing vulnerability to rice productivity.

The selected substitutive variables were derived through in-depth prior inventions and several expert consultations and inventor meetings, which can be referred to by local governments in selecting surrogate variables for vulnerability assessment.

In particular, by referring to the detailed substitution variables that compose adaptability, it is possible to more clearly identify the parts necessary to reduce vulnerability to specific sectors and specific sub-categories. For example, areas with high probability of landslides can contribute to lowering vulnerability by increasing adaptation capacity, such as forest control area and the number of forest officials.

Through the present invention, in order to support detailed implementation plans for adaptation measures for adaptation to climate change by local governments, climate change vulnerability maps of each sector were produced.

Vulnerability maps can be used to compare the relative vulnerabilities of each sub-category in every sub-category, which can help local governments identify sub-categories that are considered more vulnerable than other regions in Korea and reflect them in adaptation measures. However, it is not possible to compare items between absolutes of the presented vulnerability indexes.

The climate impact (climate exposure + sensitivity) map, which is provided along with the vulnerability map, shows only the effects predicted from climate change and environmental factors, except for adaptation ability.

If it is deemed to be vulnerable only by climate impacts excluding adaptation capacity, or it is difficult to consider adaptation capacity, local governments can identify items that need to be adapted based on climate impact maps.

Based on the vulnerability assessment results, a statistical analysis was performed by creating a radiation graph.

Radiation graphs show the contribution of surrogate variables reflected in vulnerability assessments, showing the contribution of climate exposure, sensitivity and adaptability to vulnerability, and the contribution of detailed surrogate variables to surrogate variables.

Radiation graphs can identify which surrogate variables contribute to the vulnerability of a particular sub-category, and can also identify the contribution of the sub-substitutes to the surrogate variables (eg, the average annual rainfall contribution to climate exposure). .

Therefore, local governments can identify which surrogate is affected by the influence of sub-variables and sub-substitutes, and can refer to them in deciding which surrogate variables to focus on when developing adaptation measures.

The climate change vulnerability assessment tool utilized in the present invention is designed to ensure maximum accessibility of essential basic data for comprehensive and systematic impact assessment and adaptation measures for climate change. This evaluation tool is aimed at local government policy makers, stakeholders, and experts, and it is possible to add data and adjust weights to reflect local government characteristics.

In particular, the sensitivity and adaptability surrogate variables can be modified freely by considering the characteristics of each climate change impact item and region. This enables vulnerability assessments that reflect more detailed and relevant data for each municipality.

The weight set by default in the evaluation tool is derived from two expert Delphi surveys and reflects the opinions of experts in the field. However, in some cases, when weights need to be modified to reflect regional characteristics, the weight can be adjusted by using the editing function.

It will be understood by those skilled in the art that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. The scope of the present invention is indicated by the scope of the following claims rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and the equivalent concept are included in the scope of the present invention. Should be interpreted

112: climate exposure variable storage unit 114: sensitivity variable storage unit
116: adaptive capacity variable storage unit 118: administrative area storage unit
120: city and district data conversion unit 132: substitution variable storage unit
134: weight storage unit 142: substitute variable normalization unit
144: vulnerability index calculation unit 146: vulnerability index standardization unit
148: vulnerability standard value comparison unit 200: display unit

Claims (26)

A climate exposure variable storage unit for storing climate exposure variables, which are variables representing impacts of climate change, by division into health, forest, ecosystem, agriculture, water management, marine and fisheries, and disaster sectors;
Sensitivity variables, which are variables representing the degree of impact by climate exposure variables, are divided into health, forest, ecosystem, agriculture, water management, marine and fisheries, and disaster sectors and stored in grid units. part;
Adaptation capacity that can reduce the effects of climate exposure and sensitivity variables is divided into health sector, forest sector, ecosystem sector, agriculture sector, water management sector, marine / fishery sector and disaster sector and stored in grid units. Capability variable storage;
An administrative district storage unit for storing boundary data of the administrative district;
Referring to the administrative boundary boundary data stored in the administrative region storage unit, the climate exposure variable for each administrative region is generated through the climate exposure variable stored in the climate exposure variable storage unit, and the administration is performed through the sensitivity variable stored in the sensitivity variable storage unit. A city and district data conversion unit for generating a sensitivity variable for each zone and generating an adaptive capability variable for each administrative region through the adaptive capability variable stored in the adaptive capability variable storage unit;
A surrogate variable storage unit for storing climate-exposure variables for each administrative region, sensitivity variables for each administrative region, and adaptive capability variables for each administrative region generated by the city and district data conversion unit;
Weights for climate exposure variables, sensitivity variables, adaptability variables and detailed substitutions within each surrogate variable to assess municipal vulnerability by health sector, forest sector, ecosystem sector, agriculture sector, water management sector, marine and fisheries sector and disaster sector. A weight storage unit for storing a weight for the variable;
A substitute variable standardizing unit for deriving a substitute variable standardization value by standardizing the substitute variable stored in the substitute variable storage unit;
A vulnerability index calculator for calculating a vulnerability index by applying a weight stored in the weight storage unit to a substitute variable standardized value derived from the substitute variable standardization unit;
A vulnerability index standardization unit for deriving a vulnerability index standardization value by standardizing a vulnerability index calculated by the vulnerability index calculation unit;
A vulnerability standardization value comparing unit comparing the vulnerability index standardized value standardized by the vulnerability index standardizing unit for each administrative region;
Display administrative district geographic information from the administrative boundary boundary data stored in the administrative zone storage unit, climate exposure variable, administrative sensitivity parameter, adaptive capacity variable or vulnerability index of each administrative zone, stored in the surrogate variable storage unit, Climate change vulnerability assessment system comprising a; display unit for displaying the vulnerability index standardized value together with the administrative district geographic information.
The method of claim 1,
The city and district data conversion unit converts the climate exposure variable, the sensitivity variable and the adaptive capacity variable in the grid unit into the climate exposure variable for each administrative district, the sensitivity variable for each administrative district, and the adaptive capacity variable for each administrative district,
Prediction data value (C (i)) for each grid from the climate exposure variable stored in the climate exposure variable storage unit, the sensitivity variable stored in the sensitivity variable storage unit or the adaptive capacity variable stored in the adaptability variable storage unit, and the administrative area storage. Variable data for each administrative district (Y) for the area (S (i)) of each grid overlapping each administrative district from the administrative boundary boundary data stored in the department and the number (N) of each grid overlapping each administrative district (i)),

Is calculated as
The variable data are climate exposure variables, sensitivity variables or adaptability variables,
The variable data for each administrative district is a climate change vulnerability evaluation system, characterized in that the climate exposure variable, administrative sensitivity parameters or adaptive capacity variable by administrative district.
The method of claim 1, wherein the weight storage unit,
Two rounds of Delphi surveys by health, forest, ecosystem, agriculture, water management, marine and fisheries, and disasters, weighted the surrogate variables of climate exposure, sensitivity, and adaptability, and details within each category. Climate change vulnerability assessment system, characterized in that the weights for the surrogate variables are derived and applied.
The method of claim 1, wherein the surrogate variable normalization unit,
Characterize the surrogate variables with values ranging from 0 to 1 using a standardized formula,
The standardized formula is

Climate change vulnerability assessment system.
The method of claim 1, wherein the vulnerability index,
As a difference between climate impact and adaptability variables, composed of climate exposure and sensitivity variables,

(α, β, γ means weight)
Climate change vulnerability assessment system, characterized by
The method of claim 1, wherein the vulnerability index standardization unit,
Characterize the vulnerability index using a standardized formula to a value in the range of 0 to 1,
The standardized formula is

Climate change vulnerability assessment system.
The method of claim 1, wherein the health portion,
For details, Health vulnerability items due to floods, Health vulnerability items due to typhoons, Health vulnerability items due to heat waves, Health vulnerability items due to cold waves, Health vulnerability items due to rising ozone concentrations, Health vulnerability items due to fine dust, Other atmospheres Includes health vulnerability items by pollutants, infectious disease health vulnerability items by insects and rodents, and health vulnerability items by waterborne mediated diseases,
Health vulnerability item by the flood mentioned above,
Climatic exposure variables including daily maximum precipitation (mm), number of days (80) with more than 80 mm of precipitation, flooding area (ha) due to flooding, population over 65 (people), population under 13 (people), The percentage of the elderly living alone (% of the population), the proportion of basic beneficiaries (%), the lowland area below 10m (km2), the lowland household below 10m (households), the flood damage population (persons), and the number of waterborne diseases (persons) Sensitivity variable, Gross Domestic Product (GRDP) (million won), financial independence (%), health insurance coverage (%), health center workforce per population (persons / ten thousand), emergency medical institutions per population (unit / million), Includes adaptive capacity variables, including GRDP health and social services (million won),
Health vulnerability items caused by the typhoon,
Climate exposure variables, including daily maximum precipitation (mm), number of days with daily precipitation above 80 mm, number of days with daily maximum wind speed of 14 m / s or more, population over 65 years (persons), 13 years old Including population below (persons), elderly living alone (total population) (%), proportion of basic beneficiaries (%), lowland area below 10m (km2), lowland households below 10m (households), number of waterborne diseases (persons) Sensitivity Variables, Regional Gross Domestic Product (GRDP) (KRW Million), Financial Independence (%), Health Insurance Covered Population Ratio (%), Health Center Workforce (P / M), Number of Emergency Medical Services per Population (P / M), GRDP Includes adaptive capacity variables, including health care and social services (KRW million),
Health vulnerability item by the heat wave mentioned above,
Daily high temperature (℃), number of days with daily maximum temperature of 33 ℃ or more, times of days with daily minimum temperature of 25 ℃ or more, haptic temperature (℃), discomfort index (-), heat wave index ( Climate change variables, including relative humidity (%), population aged 65 and older, population aged 13 and under, elderly population living alone (%), primary beneficiary ratio (%), cardiovascular Sensitivity variables, including disease deaths (number of people), heat deaths / heatstroke deaths, regional gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), per population Includes adaptive capacity variables including health center workforce (persons / million), number of emergency medical services per population (unit / million), GRDP health care and social services (million won),
Health vulnerability item by the cold wave mentioned above,
The number of days when the minimum temperature is below zero, the number of days when the average daily temperature is below zero, the maximum number of consecutive un precipitation days, and the number of days when the maximum wind speed is more than 14 m / s. ), Climate exposure variables including snow cover (cm), population over 65 (people), population under 13 (people), elderly living alone (% of total population) (%), basic living beneficiaries (%), respiratory diseases Number of inpatients (persons), deaths of cerebrovascular disease (persons), adaptive capacity, gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), health center personnel per population (person) Per million population), number of emergency medical services per population (number / million), GRDP health care and social services (million won),
Health vulnerability item by the ozone concentration rise mentioned above,
Climate exposure variables including daily maximum temperature (° C), number of days when the time ozone concentration is above 100 ppb, number of days when ozone warning is issued (days), and number of days when the 8 hour cumulative ozone concentration is above 60 ppb / 8hr. , Population 65 years or older (persons), population 13 years or younger (persons), elderly living alone (total population) (%), basic recipients of living (%), respiratory disease inpatients (persons), cardiovascular death toll (persons) ), Including sensitivity variables, Gross Domestic Product (GRDP) (million won), financial independence (%), health insurance coverage (%), health center workforce per population (persons / ten thousand), number of emergency medical institutions per population (dog / 1 million people), including GRDP health care and social services (million won).
Health vulnerability item by the fine dust,
Climate exposure variables including daily maximum temperature (℃), fine dust concentration (ug / ㎥), number of days (times) with fine dust concentration above 100 ug / ㎥, population over 65 years (persons), population under 13 years Sensitivity variable including (person), elderly person living alone (total population) (%), basic living beneficiary rate (%), number of hospitalized patients with respiratory disease (person), cardiovascular death toll (person), gross regional output (GRDP) (KRW Million), Financial Independence (%), Health Insurance Covered Population (%), Health Center Personnel Per Population (Person / Men), Emergency Medical Institutions per Population (Unit / Million), GRDP Health and Social Services (KRW Million) Include adaptive capacity variables,
Health vulnerability items caused by other air pollutants,
Highest Temperature (℃), CO (External Heating Emissions) (kg), CO (Industrial Emissions) (kg), SOx (External Heating Emissions) (kg), SOx (Industrial Emissions) (kg), NOx (Residential) Climate exposure variables including non-heating emissions (kg), NOx (industrial emissions) (kg), population over 65 (people), population under 13 (people), elderly living alone (% of total population), basis Percentage of living beneficiaries, inpatients with respiratory disease (persons), sensitivity variables including cardiovascular deaths (persons), gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), The number of adaptive health centers per population (number / million), number of emergency medical institutions per population (unit / million), GRDP health care and social services (million won),
The infectious disease health vulnerability item by the insect and rodent,
A climate that includes the number of days with a daily maximum temperature of 33 ° C or higher, the number of days with a daily minimum temperature of 25 ° C or higher, the maximum daily precipitation (mm), and the number of days with a daily precipitation greater than 80mm. Exposure variable, population 65 years or older (people), population 13 years or younger (people), elderly living alone (total population) (%), basic beneficiaries (%), annual average malaria incidence (persons), annual average Tsutsugamus disease Sensitivity variables, including number of people affected, gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage rate (%), health center workforce per population (persons / ten thousand), emergency medical institutions per population Includes adaptive capacity variables, including numbers (million / million), GRDP health and social services (million won),
Health vulnerability items for the waterborne mediated disease,
Climate that includes the number of days (times) with a daily maximum temperature of 33 ° C or more, the number of days (times) with a daily minimum temperature of 25 ° C or more, the maximum daily precipitation (mm), and the number of times (days) with a daily precipitation of 80 mm or more Sensitivity variables, including exposure variables, population aged 65 and older, population aged 13 and older, elderly living alone (total population) (%), basic livelihood percent (%), number of waterborne patients (persons), Gross Regional Product (GRDP) (million won), Financial Independence (%), Health Insurance Covered Population Ratio (%), Health Center Workforce (Pop / Million), Number of Emergency Medical Services per Population (P / M), GRDP Health Care and Society A climate change vulnerability assessment system comprising adaptive capacity variables including welfare services (million won).
The method of claim 1, wherein the forest sector,
Landslide items due to heavy rain, vulnerabilities of forest roads by landslides, items of vulnerabilities to forest fires, items of vulnerabilities of pine trees by pests, items of vulnerabilities of pine and matsutake mushrooms, items of vulnerabilities of forest productivity, forests by drought Contains the vulnerability items of vegetation,
Landslide item by the heavy rain,
Climate exposure variables, including the number of times of daily precipitation above 80 mm, daily maximum precipitation in mm, precipitation in June through August in mm, and maximum daily precipitation in 5 days in mm, average slope in the forest ), Conifer area (ha), average altitude in forest (m), sensitivity variables including freeland area (ha), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) (million won) ), Including adaptive capacity variables, including financial independence (%),
Vulnerability item of forest road by the landslide mentioned above,
Climate exposure variables including the number of days (80 times) with daily precipitation greater than 80 mm, daily maximum precipitation (mm), June-August precipitation (mm), and 5-day maximum precipitation (mm), average slope in the forest (degrees) ), Sensitivity variable including conifer area (ha), mean altitude in forest (m), freeland area (ha), distance of forest road (m), number of forest officials (persons), forest control area (ha), region Includes adaptive capacity variables, including gross domestic product (GRDP) (in million won) and financial independence (%),
Vulnerability items for forest fires,
The number of times the maximum daily temperature is 33 ℃ or more, the number of times the maximum daily wind speed is 14m / s or more, the number of times the effective humidity is 35% or less, and the maximum number of consecutive un precipitation days Climate exposure variables including (times), conifer area (ha), hardwood area (ha), mixed forest area (ha), soil moisture 10 cm (m³ / m³), average slope in forests (degrees), and total population (persons) Includes adaptive variables including sensitivity variables, number of forest officials (persons), forest control area (ha), regional gross domestic product (GRDP) (million won), financial independence (%),
The fragility item of the pine by the pest mentioned above,
Precipitation (mm) from June to August (summer season), daily minimum temperature (℃) from June to August (summer season), daily maximum temperature (℃) from June to August (summer season), daily maximum wind speed is 14m / s Climate exposure variables that include the number of days that are abnormal, pine area (m²), average slope in forests (degrees), average elevation in forests (m), sensitivity variables including pest incidence (ha), forests Includes adaptive capacity variables including the number of civil servants, pest control employees, forest control area (ha), regional gross domestic product (GRDP) (million won), and financial independence (%),
The fragile items of the pine and Matsutake,
June-August Precipitation (mm), June-August Daily Average Temperature (℃), Precipitation (mm / day), Soil Moisture 10cm (m³ / m³), Maximum No. of Consecutive Precipitation Days (times) Climate exposure variables, pine area (m²), average altitude in forests (m), sensitivity variables including forest by-products production (tons), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) ) (In millions of dollars), including financial independence (%),
The vulnerability item of forest productivity is
Climate exposure variables including precipitation (mm / day), daily maximum temperature (° C), daily minimum temperature (° C), and the maximum number of consecutive rainless days, conifer area (ha), hardwood area (ha) , Sensitivity variable including mixed forest area (ha), number of forest officials (person), forest control area (ha), natural rest year implementation area (m²), natural forest nursery (ha), financial independence (%), gross domestic product Include adaptive capacity variables, including (GRDP) (million won),
Items of vulnerability of forest vegetation caused by the drought,
Climate exposure variables, conifer area (ha), deciduous tree area (ha), including precipitation (mm / day), the maximum number of consecutive days of no precipitation, and the number of days (effectiveness) of less than 35% effective humidity. Mixed forest area (ha), sensitivity variable including plantation area (ha), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) (million won), financial independence (%), natural forest care Climate change vulnerability assessment system, characterized by including adaptive capacity variables, including (ha).
The method of claim 1, wherein the ecosystem section,
The sub-items include the vulnerability of conifers (vulnerabilities of tree growth and distribution), the vulnerability of insects, and the vulnerability of national parks (vulnerabilities of conservation zone management).
The fragility (vulnerability of tree growth and distribution) item of the conifer,
Includes January-March Daily Average Temperature (° C), Temperature (° C), Precipitation (mm / day), June-August Daily Average Temperature (° C), June-August Daily Highest Temperature (° C) Climate exposure variables, conifer distribution area (ha), coniferous wood production (㎥), coniferous forest by-product production (㎥), forest-related population (persons), timber felling area (ha), agriculture and forestry businesses (number) , Sensitivity variables including the number of agricultural and forestry workers (persons), adaptive capacity variables including conifer planting area (ha), natural forest nursery (ha), and number of forestry officials (persons),
The vulnerability item of the insect,
January-March Daily Average Temperature (° C), Number of Days with Sub-zero Average Temperature (times), April Daily Average Temperature (° C), April Average Relative Humidity (%), Consecutive Precipitation Days Climate exposure variables, including maximum values (times), evapotranspiration (mm / day), insolation (W / m2), daily average temperature (° C) in June and August, forest pests (number), and forest pathogens-Fusarium branches Sensitivity variables including blight (number), bee farmers (dog), bee breeding scale (bucket), number of insect-borne infectious diseases (persons), pest damage logging area (ha), pest damage harvesting amount (㎥), pest control period -Adaptability capacity variables including small pear tree (accumulated), forest control area (ha), pest control employees (persons), bio industry (dogs), eco-friendly fruit farmers (households), eco-friendly fruit farmers (households) Including,
Vulnerability (vulnerabilities of conservation area management) item of the national park mentioned above,
Precipitation (mm / day), the maximum number of consecutive un precipitation days (times), the number of times the daily average temperature is below freezing (times), the number of times the daily maximum temperature is above 33 ℃ (times), March to May Precipitation (mm / day), June-August precipitation (mm / day), September-November precipitation (mm / day), December-February precipitation (mm / day), Number of days with more than 80 mm of precipitation ), Climate exposure variables including the number of days with a maximum wind speed of 14 m / s or more, the number of species of plants in the national park, the number of species of animals in the national park, the number of visitors, and the visitors Sensitivity variables including year-on-year increase and decrease (%), endangered species (species), endangered species (species), and number of administrative districts to be linked for national park management, number of national park staff (persons) , Number of national park organizations (number), number of national park offices (number), national park area increase / decrease (count), national park commentary operation frequency (times), national park temple area (㎢), natural rest year enforcement area (㎡) , Including natural rest year enforcement distance (km) Climate Change vulnerability assessment system, comprising the adaptation parameters.
The method of claim 1, wherein the agricultural sector,
The sub-items include the vulnerability item of cropland soil erosion, the vulnerability item of cultivation / breeding facility collapse, the item of vulnerability of rice productivity, the item of vulnerability of apple productivity, the item of vulnerability of livestock productivity,
The vulnerability item of the farmland soil erosion,
Climate exposure variables, including the amount of precipitation (mm / day), the number of days with more than 80 mm of precipitation, the number of days of precipitation (more than 10 mm) (times), field area (ha), field area (ha), area Sensitivity variables including the average slope (deg), number of farm machinery holdings per acreage (number / ha), number of farmers using PCs, total number of farms, number of agricultural population per acreage (people / ha), maintenance staff per farmland (people / ha) Include adaptive capacity variables, including per capita gross domestic product (GRDP) (million won), financial independence (%),
The vulnerability item of the cultivation / breeding facility collapse,
Climate exposure parameters including the number of days (maximum) with wind speeds of 14 m / s or more, the number of days with snowfall of 20 cm or more, the number of times with day precipitation of 160 mm or more, and the area of plant crops (ha) ), Sensitivity variables including breeding facility area (m²), house damage area per plant crop area, livestock farming area per plant area (location / ha), agricultural population per person / cultivation area (person / ha), PC utilization Includes adaptive capacity variables, including the number of farmers / total number of farms, the number of farm-related maintenance workers (persons / ha), per capita regional GDP (million won), and financial independence (%),
The vulnerability item of the rice productivity mentioned above,
The number of times when the minimum temperature is 13 ° C or lower in April to June, the number of times the minimum temperature is 17 ° C or lower in July, September, and the number of days when the minimum temperature is 14 ° C or lower in September and October (Times), the number of days when the highest temperature is 30 ° C or higher (times), the number of times the time when the ozone concentration is 100 ppb or more (times), Log (the sum of the insolation of April to October), day Climate exposure variables including the number of days with more than 160 mm of precipitation, the number of days with a maximum wind speed of more than 14 m / s, the rice paddy area (ha), the crop area per crop area, and the potential for pest damage. Sensitivity variables, rice production per ton (ha), rice yield per cultivated area (number of people / ha), arable land ratio (%), number of farmers using PC, total number of farmers, maintenance business associates per cultivated land (person), 1 Includes adaptive capacity variables, including regional GDP per capita (KRW million) and financial independence (%),
The vulnerability item of the apple productivity mentioned above,
Annual average temperature range 8 ℃ ~ 11 ℃, October daily average temperature (℃), April ~ August daily average temperature (℃), August daily average temperature (℃), April ~ August daily maximum temperature (℃), 4 Climate exposure variables including precipitation in October (mm), April-October maximum wind speed of 14 m / s (times), sensitivity variable including apple cultivation area (ha), crop area per crop area , Apple production per cultivated area (kg / 10a), fruit orchard per person per cultivated area (person / ha), number of agricultural machinery possessed per apple cultivation area (large / ha), number of farmers / apple farmers utilizing PC, maintenance staff per arable land area (person ), Including adaptive capacity variables including per capita gross domestic product (GRDP) (million won) and financial independence (%),
The vulnerability item of livestock productivity is
The number of times the daily maximum temperature is 27 ℃ or more, the number of times the temperature and humidity index is 72 or more, the number of times the snowfall is 20cm or more, and the number of days the maximum wind speed is 14m / s or more. Climate exposure variables, including livestock breeding heads (thousands), livestock livestock per animal area (number of sites / ha), sensitivity variables, including livestock disease risk, livestock livestock population (number / ha) Includes adaptive capacity variables, including livestock wastewater treatment capacity, number of farmers using PCs, number of livestock farmers, maintenance staff per farmland (person / ha), gross domestic product per capita (GRDP) (million won), and financial independence (%) Climate change vulnerability assessment system, characterized in that.
The method of claim 1, wherein the water management section,
The details include the Vulnerability section of the dimension, the Vulnerability section of the course, and the Vulnerability section for water quality and aquatic life.
Vulnerability item of the above dimensions,
Climate exposure variables including daily maximum precipitation (mm), number of days (80 times) with daily precipitation greater than 80 mm, maximum daily rainfall (mm), ground runoff (mm / day), and precipitation from June to September (mm) , Low land area less than 10 m (㎢), Low land household less than 10 m (Household), Dike use area (%), Population density (person / ㎢), Total population, Regional average slope (deg), Road area ratio (%), Sensitivity variables including flood damage (thousand won) over the last three years, flood damage population (number of people) over the last three years, financial independence (%), the number of civil servants per population (person / million), gross domestic product (GRDP) (million won) , Adaptive capacity variables including water management officials per area (person / ㎢), river yield (%), domestic drainage facility drainage capacity (㎥ / min), reservoir flood control capacity (million㎥),
The vulnerability item of the above completion,
Maximum number of consecutive precipitation days (day), December-February precipitation (mm), March-May precipitation (mm), December-February evapotranspiration (mm), March-May evapotranspiration (mm), underground runoff climate exposure variables, including (mm / day), population density (person / ㎢), total population (person), water supply: 1 liter per day (liter), grain production per ton (ton / ㎢), livestock production per area (mari) / ㎢), groundwater use (㎥ / year), river water use (㎥ / year), domestic water use (1,000㎥ / year), industrial water use (1,000㎥ / year), agricultural water use (1,000㎥ / year) Sensitivity variables included, financial independence (%), number of civil servants per population (persons / million), gross domestic product (GRDP) (million won), area water management officials (persons), water supply rate (%), groundwater capacity ( Thousand ㎥ / year), the reservoir capacity for water supply per unit area (1,000㎥), and the adaptive capacity variable including water reuse capacity per unit area (1,000㎥ / year),
Vulnerability items for the water quality and aquatic ecology,
Daily maximum temperature (℃), daily maximum precipitation (mm), number of days (times) with daily precipitation greater than 80mm, maximum number of consecutive un precipitation days (times), number of times (day) with daily maximum temperature above 33 ℃, Climate exposure variables including the number of times the minimum temperature is 25 ℃ or more, regional average slope (deg), river yield (%), population of livestock industry (name / million people), livestock production status (mari / ㎢) ), Sensitivity variables including fertilizer use per cultivated land (ton / ha), major animal species distribution (species), major plant species distribution (species), forest area rate (%), managed land rate (%), and sewerage rate ( %), Climate change vulnerability assessments including adaptive capacity variables, including civil servants per population (persons / ten thousand), population density (%), road area ratio (%), road length per unit area (km) system.
The method of claim 1, wherein the marine / fisheries sector,
It includes the items of vulnerability of fisheries (aquaculture) due to temperature change,
Items of vulnerability of fisheries (aquaculture) due to the temperature change,
Sea surface temperature (℃), annual rate of sea temperature rise (℃ / yr), the number of days when the daily average temperature is below freezing, the number of times when the daily maximum temperature is above 33 ℃, and the amount of precipitation over 80mm Climate exposure variables including frequency (times), number of times jellyfish damage occurred, aquaculture farming area (fish-sea cage) (ha), aquaculture farming area (business-sea cage) (ha), aquaculture farming facility Sensitivity including area (fish-festival) (ha), aquaculture farming facility area (business-festival) (ha), fish farming status (sea cage) (point), fish farming status (festival) (point) Variables, Financial Independence (%), Number of Public Officials per Population (Person / Ten Thousand), Farming Facility Area (Fish-Landry) (ha), Farming Facility Area (Business-Landry) (ha), Farming Fish Status ( A system for assessing vulnerability to climate change, comprising adaptation capabilities, including land tanks).
The method of claim 1, wherein the disaster section,
Details include infrastructure vulnerability items for floods, infrastructure vulnerability items for heat waves, infrastructure vulnerability items for heavy snowfall, and infrastructure vulnerability items for sea level rise.
Infrastructure vulnerability items for the flood,
The number of days with more than 80 mm / day of daily precipitation, climate exposure variables including the maximum daily precipitation, mm, road area (㎡) of flood-vulnerable areas (flooded buildings), and water supply facilities (㎡), electricity supply facility area (㎡), gas supply facility area (㎡), heat supply facility area (㎡), oil storage and oil supply facility area (㎡), sewage area (㎡), water pollution prevention facility area (㎡ Sensitivity variables including), adaptive capacity variables including per capita Gross Domestic Product (GRDP) (million won), number of civil servants per population (persons / million), and river yield (%),
Infrastructure vulnerability items for the heat wave,
Sensitivity including the number of days when the maximum daily temperature is 33 ℃ or more, the number of times the minimum temperature is 25 ℃ or more, and the road area (㎡) of the heat-vulnerable area (water impermeable area) (㎡) Variable, adaptive capacity variables including Gross Domestic Product (PDP) per million (KRW million), number of civil servants per population (person / million), and green area per person (㎡),
Infrastructure vulnerability items for heavy snow,
Climate exposure variables including snowfall (cm), road surface area (㎡) in vulnerable areas (snow damage) (1,000 won), sensitivity variables including railroad area (㎡), airport area (㎡), and gross domestic product per capita (GRDP) (million won), including adaptive capacity variables including the number of civil servants per population (persons / million),
The infrastructure vulnerability item for sea level rise is
Sensitivity including climate exposure variables including coastal water temperature increase rate (%), road area (㎡), harbor area (㎡), water pollution prevention facility area (㎡) of sea level rise vulnerable areas A climate change vulnerability assessment system comprising a variable, an adaptive capacity variable including per capita gross domestic product (GRDP) (million won), the number of civil servants per population (persons / ten thousand), and the area of assistance facilities (㎡).
(a) storing climate exposure variables, which are variables representing impacts of climate change, on a grid basis for each sector of health, forests, ecosystems, agriculture, water management, ocean / fishing and disasters;
(b) subdividing the sensitivity variable, which is a variable representing the degree of influence by the climate exposure variable, into health, forest, ecosystem, agriculture, water management, marine / fishing, and disaster sectors and storing them in grid units;
(c) storing the adaptive capacity variables that can reduce the influence of the climate exposure variable and the sensitivity variable in the unit of health, forest, ecosystem, agriculture, water management, marine / fishing, and disasters by grid unit;
(d) storing the boundary data of the administrative area;
(e) creating a climate exposure variable for each administrative region using the climate exposure variable stored in step (a) with reference to the administrative boundary data stored in step (d), and generating the sensitivity variable stored in step (b). Generating a sensitivity variable for each administrative region through the generation, and generating an adaptive capability variable for each administrative region through the adaptive capability variable stored in the step (c);
(f) storing the climate exposure variable for each administrative region, the sensitivity variable for each administrative region, and the adaptive capability variable for each administrative region generated by step (e);
(g) Weights for climate exposure variables, sensitivity variables, and adaptive capacity variables and detailed surrogate variables within each surrogate variable to assess municipal vulnerability in each sector; health, forest, ecosystem, agriculture, water management, marine / fish and disasters. Storing the weight for the;
(h) standardizing surrogate variables stored in step (f) to derive surrogate variable normalized values;
(i) calculating a vulnerability index by applying the weight stored in step (g) to the surrogate variable normalized value derived in step (h);
(j) deriving a vulnerability index standard value by standardizing the vulnerability index calculated in step (i);
(k) comparing the vulnerability index standardized values standardized in step (j) for each administrative district;
(l) displaying administrative district geographic information from the administrative boundary boundary data stored in step (d); and climate exposure variables, administrative sensitivity parameters, and adaptive capacity variables for each administrative zone; And displaying the vulnerability index and the vulnerability index standardized value together with the administrative district geographic information.
15. The method of claim 14,
In the step (e) in converting the climate exposure variable, the sensitivity variable and the adaptive capacity variable in the grid unit into the climate exposure variable by administrative district, the sensitivity variable by administrative district and the adaptive capacity variable by administrative district,
From the climate exposure variable stored in the step (a), the sensitivity variable stored in the step (b) or the adaptability variable stored in the step (c), the prediction data values (C (i)) and (d) for each grid From the stored administrative boundary data, for each grid area (S (i)) overlapping each administrative area, and the number N of each grid overlapping each administrative area, variable data for each administrative area (Y (i) )),

Is calculated as
The variable data are climate exposure variables, sensitivity variables or adaptability variables,
The variable data for each administrative district is a climate change vulnerability assessment method, characterized in that the climate change variable, administrative sensitivity parameters or adaptive capacity variable by administrative district.
The method of claim 14, wherein step (g)
Two rounds of Delphi surveys by health, forest, ecosystem, agriculture, water management, marine and fisheries, and disasters, weighted the surrogate variables of climate exposure, sensitivity, and adaptability, and details within each category. Climate change vulnerability assessment method characterized by deriving and applying weights for surrogate variables.
The method of claim 14, wherein (h) is,
Characterize the surrogate variables with values ranging from 0 to 1 using a standardized formula,
The standardized formula is

Climate change vulnerability assessment method characterized in that.
The method of claim 14, wherein the vulnerability index,
As a difference between climate impact and adaptability variables, composed of climate exposure and sensitivity variables,

(α, β, γ means weight)
A method for assessing vulnerability to climate change, characterized by
The method of claim 14, wherein step (j) is
Characterize the vulnerability index using a standardized formula to a value in the range of 0 to 1,
The standardized formula is

Climate change vulnerability assessment method characterized in that.
The method of claim 14, wherein the health portion,
For details, Health vulnerability items due to floods, Health vulnerability items due to typhoons, Health vulnerability items due to heat waves, Health vulnerability items due to cold waves, Health vulnerability items due to rising ozone concentrations, Health vulnerability items due to fine dust, Other atmospheres Includes health vulnerability items by pollutants, infectious disease health vulnerability items by insects and rodents, and health vulnerability items by waterborne mediated diseases,
Health vulnerability item by the flood mentioned above,
Climatic exposure variables including daily maximum precipitation (mm), number of days (80) with more than 80 mm of precipitation, flooding area (ha) due to flooding, population over 65 (people), population under 13 (people), The percentage of the elderly living alone (% of the population), the proportion of basic beneficiaries (%), the lowland area below 10m (km2), the lowland household below 10m (households), the flood damage population (persons), and the number of waterborne diseases (persons) Sensitivity variable, Gross Domestic Product (GRDP) (million won), financial independence (%), health insurance coverage (%), health center workforce per population (persons / ten thousand), emergency medical institutions per population (unit / million), Includes adaptive capacity variables, including GRDP health and social services (million won),
Health vulnerability items caused by the typhoon,
Climate exposure variables, including daily maximum precipitation (mm), number of days with daily precipitation above 80 mm, number of days with daily maximum wind speed of 14 m / s or more, population over 65 years (persons), 13 years old Including population below (persons), elderly living alone (total population) (%), proportion of basic beneficiaries (%), lowland area below 10m (km2), lowland households below 10m (households), number of waterborne diseases (persons) Sensitivity Variables, Regional Gross Domestic Product (GRDP) (KRW Million), Financial Independence (%), Health Insurance Covered Population Ratio (%), Health Center Workforce (P / M), Number of Emergency Medical Services per Population (P / M), GRDP Includes adaptive capacity variables, including health care and social services (KRW million),
Health vulnerability item by the heat wave mentioned above,
Daily high temperature (℃), number of days with daily maximum temperature of 33 ℃ or more, times of days with daily minimum temperature of 25 ℃ or more, haptic temperature (℃), discomfort index (-), heat wave index ( Climate change variables, including relative humidity (%), population aged 65 and older, population aged 13 and under, elderly population living alone (%), primary beneficiary ratio (%), cardiovascular Sensitivity variables, including disease deaths (number of people), heat deaths / heatstroke deaths, regional gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), per population Includes adaptive capacity variables including health center workforce (persons / million), number of emergency medical services per population (unit / million), GRDP health care and social services (million won),
Health vulnerability item by the cold wave mentioned above,
The number of days when the minimum temperature is below zero, the number of days when the average daily temperature is below zero, the maximum number of consecutive un precipitation days, and the number of days when the maximum wind speed is more than 14 m / s. ), Climate exposure variables including snow cover (cm), population over 65 (people), population under 13 (people), elderly living alone (% of total population) (%), basic living beneficiaries (%), respiratory diseases Number of inpatients (persons), deaths of cerebrovascular disease (persons), adaptive capacity, gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), health center personnel per population (person) Per million population), number of emergency medical services per population (number / million), GRDP health care and social services (million won),
Health vulnerability item by the ozone concentration rise mentioned above,
Climate exposure variables including daily maximum temperature (° C), number of days when the time ozone concentration is above 100 ppb, number of days when ozone warning is issued (days), and number of days when the 8 hour cumulative ozone concentration is above 60 ppb / 8hr. , Population 65 years or older (persons), population 13 years or younger (persons), elderly living alone (total population) (%), basic recipients of living (%), respiratory disease inpatients (persons), cardiovascular death toll (persons) ), Including sensitivity variables, Gross Domestic Product (GRDP) (million won), financial independence (%), health insurance coverage (%), health center workforce per population (persons / ten thousand), number of emergency medical institutions per population (dog / 1 million people), including GRDP health care and social services (million won).
Health vulnerability item by the fine dust,
Climate exposure variables including daily maximum temperature (℃), fine dust concentration (ug / ㎥), number of days (times) with fine dust concentration above 100 ug / ㎥, population over 65 years (persons), population under 13 years Sensitivity variable including (person), elderly person living alone (total population) (%), basic living beneficiary rate (%), number of hospitalized patients with respiratory disease (person), cardiovascular death toll (person), gross regional output (GRDP) (KRW Million), Financial Independence (%), Health Insurance Covered Population (%), Health Center Personnel Per Population (Person / Men), Emergency Medical Institutions per Population (Unit / Million), GRDP Health and Social Services (KRW Million) Include adaptive capacity variables,
Health vulnerability items caused by other air pollutants,
Highest Temperature (℃), CO (External Heating Emissions) (kg), CO (Industrial Emissions) (kg), SOx (External Heating Emissions) (kg), SOx (Industrial Emissions) (kg), NOx (Residential) Climate exposure variables including non-heating emissions (kg), NOx (industrial emissions) (kg), population over 65 (people), population under 13 (people), elderly living alone (% of total population), basis Percentage of living beneficiaries, inpatients with respiratory disease (persons), sensitivity variables including cardiovascular deaths (persons), gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage (%), The number of adaptive health centers per population (number / million), number of emergency medical institutions per population (unit / million), GRDP health care and social services (million won),
The infectious disease health vulnerability item by the insect and rodent,
A climate that includes the number of days with a daily maximum temperature of 33 ° C or higher, the number of days with a daily minimum temperature of 25 ° C or higher, the maximum daily precipitation (mm), and the number of days with a daily precipitation greater than 80mm. Exposure variable, population 65 years or older (people), population 13 years or younger (people), elderly living alone (total population) (%), basic beneficiaries (%), annual average malaria incidence (persons), annual average Tsutsugamus disease Sensitivity variables, including number of people affected, gross domestic product (GRDP) (million won), financial independence (%), health insurance coverage rate (%), health center workforce per population (persons / ten thousand), emergency medical institutions per population Includes adaptive capacity variables, including numbers (million / million), GRDP health and social services (million won),
Health vulnerability items for the waterborne mediated disease,
Climate that includes the number of days (times) with a daily maximum temperature of 33 ° C or more, the number of days (times) with a daily minimum temperature of 25 ° C or more, the maximum daily precipitation (mm), and the number of times (days) with a daily precipitation of 80 mm or more Sensitivity variables, including exposure variables, population aged 65 and older, population aged 13 and older, elderly living alone (total population) (%), basic livelihood percent (%), number of waterborne patients (persons), Gross Regional Product (GRDP) (million won), Financial Independence (%), Health Insurance Covered Population Ratio (%), Health Center Workforce (Pop / Million), Number of Emergency Medical Services per Population (P / M), GRDP Health Care and Society Climate change vulnerability assessment method comprising the adaptive capacity variable including the welfare service industry (million won).
The method of claim 14, wherein the forest sector,
Landslide items due to heavy rain, vulnerabilities of forest roads by landslides, items of vulnerabilities to forest fires, items of vulnerabilities of pine trees by pests, items of vulnerabilities of pine and matsutake mushrooms, items of vulnerabilities of forest productivity, forests by drought Contains the vulnerability items of vegetation,
Landslide item by the heavy rain,
Climate exposure variables, including the number of times of daily precipitation above 80 mm, daily maximum precipitation in mm, precipitation in June through August in mm, and maximum daily precipitation in 5 days in mm, average slope in the forest ), Conifer area (ha), average altitude in forest (m), sensitivity variables including freeland area (ha), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) (million won) ), Including adaptive capacity variables, including financial independence (%),
Vulnerability item of forest road by the landslide mentioned above,
Climate exposure variables including the number of days (80 times) with daily precipitation greater than 80 mm, daily maximum precipitation (mm), June-August precipitation (mm), and 5-day maximum precipitation (mm), average slope in the forest (degrees) ), Sensitivity variable including conifer area (ha), mean altitude in forest (m), freeland area (ha), distance of forest road (m), number of forest officials (persons), forest control area (ha), region Includes adaptive capacity variables, including gross domestic product (GRDP) (in million won) and financial independence (%),
Vulnerability items for forest fires,
The number of times the maximum daily temperature is 33 ℃ or more, the number of times the maximum daily wind speed is 14m / s or more, the number of times the effective humidity is 35% or less, and the maximum number of consecutive un precipitation days Climate exposure variables including (times), conifer area (ha), hardwood area (ha), mixed forest area (ha), soil moisture 10 cm (m³ / m³), average slope in forests (degrees), and total population (persons) Includes adaptive variables including sensitivity variables, number of forest officials (persons), forest control area (ha), regional gross domestic product (GRDP) (million won), financial independence (%),
The fragility item of the pine by the pest mentioned above,
Precipitation (mm) from June to August (summer season), daily minimum temperature (℃) from June to August (summer season), daily maximum temperature (℃) from June to August (summer season), daily maximum wind speed is 14m / s Climate exposure variables that include the number of days that are abnormal, pine area (m²), average slope in forests (degrees), average elevation in forests (m), sensitivity variables including pest incidence (ha), forests Includes adaptive capacity variables including the number of civil servants, pest control employees, forest control area (ha), regional gross domestic product (GRDP) (million won), and financial independence (%),
The fragile items of the pine and Matsutake,
June-August Precipitation (mm), June-August Daily Average Temperature (℃), Precipitation (mm / day), Soil Moisture 10cm (m³ / m³), Maximum No. of Consecutive Precipitation Days (times) Climate exposure variables, pine area (m²), average altitude in forests (m), sensitivity variables including forest by-products production (tons), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) ) (In millions of dollars), including financial independence (%),
The vulnerability item of forest productivity is
Climate exposure variables including precipitation (mm / day), daily maximum temperature (° C), daily minimum temperature (° C), and the maximum number of consecutive rainless days, conifer area (ha), hardwood area (ha) , Sensitivity variable including mixed forest area (ha), number of forest officials (person), forest control area (ha), natural rest year implementation area (m²), natural forest nursery (ha), financial independence (%), gross domestic product Include adaptive capacity variables, including (GRDP) (million won),
Items of vulnerability of forest vegetation caused by the drought,
Climate exposure variables, conifer area (ha), deciduous tree area (ha), including precipitation (mm / day), the maximum number of consecutive days of no precipitation, and the number of days (effectiveness) of less than 35% effective humidity. Mixed forest area (ha), sensitivity variable including plantation area (ha), number of forest officials (persons), forest control area (ha), gross domestic product (GRDP) (million won), financial independence (%), natural forest care climate change vulnerability assessment method characterized by including the adaptive capacity variable including (ha).
The method of claim 14, wherein the ecosystem section,
The sub-items include the vulnerability of conifers (vulnerabilities of tree growth and distribution), the vulnerability of insects, and the vulnerability of national parks (vulnerabilities of conservation zone management).
The fragility (vulnerability of tree growth and distribution) item of the conifer,
Includes January-March Daily Average Temperature (° C), Temperature (° C), Precipitation (mm / day), June-August Daily Average Temperature (° C), June-August Daily Highest Temperature (° C) Climate exposure variables, conifer distribution area (ha), coniferous wood production (㎥), coniferous forest by-product production (㎥), forest-related population (persons), timber felling area (ha), agriculture and forestry businesses (number) , Sensitivity variables including the number of agricultural and forestry workers (persons), adaptive capacity variables including conifer planting area (ha), natural forest nursery (ha), and number of forestry officials (persons),
The vulnerability item of the insect,
January-March Daily Average Temperature (° C), Number of Days with Sub-zero Average Temperature (times), April Daily Average Temperature (° C), April Average Relative Humidity (%), Consecutive Precipitation Days Climate exposure variables, including maximum values (times), evapotranspiration (mm / day), insolation (W / m2), daily average temperature (° C) in June and August, forest pests (number), and forest pathogens-Fusarium branches Sensitivity variables including blight (number), bee farmers (dog), bee breeding scale (bucket), number of insect-borne infectious diseases (persons), pest damage logging area (ha), pest damage harvesting amount (㎥), pest control period -Adaptability capacity variables including small pear tree (accumulated), forest control area (ha), pest control employees (persons), bio industry (dogs), eco-friendly fruit farmers (households), eco-friendly fruit farmers (households) Including,
Vulnerability (vulnerabilities of conservation area management) item of the national park mentioned above,
Precipitation (mm / day), the maximum number of consecutive un precipitation days (times), the number of times the daily average temperature is below freezing (times), the number of times the daily maximum temperature is above 33 ℃ (times), March to May Precipitation (mm / day), June-August precipitation (mm / day), September-November precipitation (mm / day), December-February precipitation (mm / day), Number of days with more than 80 mm of precipitation ), Climate exposure variables including the number of days with a maximum wind speed of 14 m / s or more, the number of species of plants in the national park, the number of species of animals in the national park, the number of visitors, and the visitors Sensitivity variables including year-on-year increase and decrease (%), endangered species (species), endangered species (species), and number of administrative districts to be linked for national park management, number of national park staff (persons) , Number of national park organizations (number), number of national park offices (number), national park area increase / decrease (count), national park commentary operation frequency (times), national park temple area (㎢), natural rest year enforcement area (㎡) , Including natural rest year enforcement distance (km) Climate Change vulnerability assessment method comprising the adaptation parameters.
The method of claim 14, wherein the agricultural sector,
The sub-items include the vulnerability item of cropland soil erosion, the vulnerability item of cultivation / breeding facility collapse, the item of vulnerability of rice productivity, the item of vulnerability of apple productivity, the item of vulnerability of livestock productivity,
The vulnerability item of the farmland soil erosion,
Climate exposure variables, including the amount of precipitation (mm / day), the number of days with more than 80 mm of precipitation, the number of days of precipitation (more than 10 mm) (times), field area (ha), field area (ha), area Sensitivity variables including the average slope (deg), number of farm machinery holdings per acreage (number / ha), number of farmers using PCs, total number of farms, number of agricultural population per acreage (people / ha), maintenance staff per farmland (people / ha) Include adaptive capacity variables, including per capita gross domestic product (GRDP) (million won), financial independence (%),
The vulnerability item of the cultivation / breeding facility collapse,
Climate exposure parameters including the number of days (maximum) with wind speeds of 14 m / s or more, the number of days with snowfall of 20 cm or more, the number of times with day precipitation of 160 mm or more, and the area of plant crops (ha) ), Sensitivity variables including breeding facility area (m²), house damage area per plant crop area, livestock farming area per plant area (location / ha), agricultural population per person / cultivation area (person / ha), PC utilization Includes adaptive capacity variables, including the number of farmers / total number of farms, the number of farm-related maintenance workers (persons / ha), per capita regional GDP (million won), and financial independence (%),
The vulnerability item of the rice productivity mentioned above,
The number of times when the minimum temperature is 13 ° C or lower in April to June, the number of times the minimum temperature is 17 ° C or lower in July, September, and the number of days when the minimum temperature is 14 ° C or lower in September and October (Times), the number of days when the highest temperature is 30 ° C or higher (times), the number of times the time when the ozone concentration is 100 ppb or more (times), Log (the sum of the insolation of April to October), day Climate exposure variables including the number of days with more than 160 mm of precipitation, the number of days with a maximum wind speed of more than 14 m / s, the rice paddy area (ha), the crop area per crop area, and the potential for pest damage. Sensitivity variables, rice production per ton (ha), rice yield per cultivated area (number of people / ha), arable land ratio (%), number of farmers using PC, total number of farmers, maintenance business associates per cultivated land (person), 1 Includes adaptive capacity variables, including regional GDP per capita (KRW million) and financial independence (%),
The vulnerability item of the apple productivity mentioned above,
Annual average temperature range 8 ℃ ~ 11 ℃, October daily average temperature (℃), April ~ August daily average temperature (℃), August daily average temperature (℃), April ~ August daily maximum temperature (℃), 4 Climate exposure variables including precipitation in October (mm), April-October maximum wind speed of 14 m / s (times), sensitivity variable including apple cultivation area (ha), crop area per crop area , Apple production per cultivated area (kg / 10a), fruit orchard per person per cultivated area (person / ha), number of agricultural machinery possessed per apple cultivation area (large / ha), number of farmers / apple farmers utilizing PC, maintenance staff per arable land area (person ), Including adaptive capacity variables including per capita gross domestic product (GRDP) (million won) and financial independence (%),
The vulnerability item of livestock productivity is
The number of times the daily maximum temperature is 27 ℃ or more, the number of times the temperature and humidity index is 72 or more, the number of times the snowfall is 20cm or more, and the number of days the maximum wind speed is 14m / s or more. Climate exposure variables, including livestock breeding heads (thousands), livestock livestock per animal area (number of sites / ha), sensitivity variables, including livestock disease risk, livestock livestock population (number / ha) Includes adaptive capacity variables, including livestock wastewater treatment capacity, number of farmers using PCs, number of livestock farmers, maintenance staff per farmland (person / ha), gross domestic product per capita (GRDP) (million won), and financial independence (%) Climate change vulnerability assessment method characterized in that.
The method of claim 14, wherein the water management section,
The details include the Vulnerability section of the dimension, the Vulnerability section of the course, and the Vulnerability section for water quality and aquatic life.
Vulnerability item of the above dimensions,
Climate exposure variables including daily maximum precipitation (mm), number of days (80 times) with daily precipitation greater than 80 mm, maximum daily rainfall (mm), ground runoff (mm / day), and precipitation from June to September (mm) , Low land area less than 10 m (㎢), Low land household less than 10 m (Household), Dike use area (%), Population density (person / ㎢), Total population, Regional average slope (deg), Road area ratio (%), Sensitivity variables including flood damage (thousand won) over the last three years, flood damage population (number of people) over the last three years, financial independence (%), the number of civil servants per population (person / million), gross domestic product (GRDP) (million won) , Adaptive capacity variables including water management officials per area (person / ㎢), river yield (%), domestic drainage facility drainage capacity (㎥ / min), reservoir flood control capacity (million㎥),
The vulnerability item of the above completion,
Maximum number of consecutive precipitation days (day), December-February precipitation (mm), March-May precipitation (mm), December-February evapotranspiration (mm), March-May evapotranspiration (mm), underground runoff climate exposure variables, including (mm / day), population density (person / ㎢), total population (person), water supply: 1 liter per day (liter), grain production per ton (ton / ㎢), livestock production per area (mari) / ㎢), groundwater use (㎥ / year), river water use (㎥ / year), domestic water use (1,000㎥ / year), industrial water use (1,000㎥ / year), agricultural water use (1,000㎥ / year) Sensitivity variables included, financial independence (%), number of civil servants per population (persons / million), gross domestic product (GRDP) (million won), area water management officials (persons), water supply rate (%), groundwater capacity ( Thousand ㎥ / year), the reservoir capacity for water supply per unit area (1,000㎥), and the adaptive capacity variable including water reuse capacity per unit area (1,000㎥ / year),
Vulnerability items for the water quality and aquatic ecology,
Daily maximum temperature (℃), daily maximum precipitation (mm), number of days (times) with daily precipitation greater than 80mm, maximum number of consecutive un precipitation days (times), number of times (day) with daily maximum temperature above 33 ℃, Climate exposure variables including the number of times the minimum temperature is 25 ℃ or more, regional average slope (deg), river yield (%), population of livestock industry (name / million people), livestock production status (mari / ㎢) ), Sensitivity variables including fertilizer use per cultivated land (ton / ha), major animal species distribution (species), major plant species distribution (species), forest area rate (%), managed land rate (%), and sewerage rate ( %), Climate change vulnerability assessments including adaptive capacity variables, including civil servants per population (persons / ten thousand), population density (%), road area ratio (%), road length per unit area (km) Way.
The method of claim 14, wherein the marine / fisheries sector,
It includes the items of vulnerability of fisheries (aquaculture) due to temperature change,
Items of vulnerability of fisheries (aquaculture) due to the temperature change,
Sea surface temperature (℃), annual rate of sea temperature rise (℃ / yr), the number of days when the daily average temperature is below freezing, the number of times when the daily maximum temperature is above 33 ℃, and the amount of precipitation over 80mm Climate exposure variables including frequency (times), number of times jellyfish damage occurred, aquaculture farming area (fish-sea cage) (ha), aquaculture farming area (business-sea cage) (ha), aquaculture farming facility Sensitivity including area (fish-festival) (ha), aquaculture farming facility area (business-festival) (ha), fish farming status (sea cage) (point), fish farming status (festival) (point) Variables, Financial Independence (%), Number of Public Officials per Population (Person / Ten Thousand), Farming Facility Area (Fish-Landry) (ha), Farming Facility Area (Business-Landry) (ha), Farming Fish Status ( A method for assessing vulnerability to climate change, comprising adaptation capabilities, including land tanks).
The method of claim 14, wherein the disaster section,
Details include infrastructure vulnerability items for floods, infrastructure vulnerability items for heat waves, infrastructure vulnerability items for heavy snowfall, and infrastructure vulnerability items for sea level rise.
Infrastructure vulnerability items for the flood,
The number of days with more than 80 mm / day of daily precipitation, climate exposure variables including the maximum daily precipitation, mm, road area (㎡) of flood-vulnerable areas (flooded buildings), and water supply facilities (㎡), electricity supply facility area (㎡), gas supply facility area (㎡), heat supply facility area (㎡), oil storage and oil supply facility area (㎡), sewage area (㎡), water pollution prevention facility area (㎡ Sensitivity variables including), adaptive capacity variables including per capita Gross Domestic Product (GRDP) (million won), number of civil servants per population (persons / million), and river yield (%),
Infrastructure vulnerability items for the heat wave,
Sensitivity including the number of days when the maximum daily temperature is 33 ℃ or more, the number of times the minimum temperature is 25 ℃ or more, and the road area (㎡) of the heat-vulnerable area (water impermeable area) (㎡) Variable, adaptive capacity variables including Gross Domestic Product (PDP) per million (KRW million), number of civil servants per population (person / million), and green area per person (㎡),
Infrastructure vulnerability items for heavy snow,
Climate exposure variables including snowfall (cm), road surface area (㎡) in vulnerable areas (snow damage) (1,000 won), sensitivity variables including railroad area (㎡), airport area (㎡), and gross domestic product per capita (GRDP) (million won), including adaptive capacity variables including the number of civil servants per population (persons / million),
The infrastructure vulnerability item for sea level rise is
Sensitivity including climate exposure variables including coastal water temperature increase rate (%), road area (㎡), harbor area (㎡), water pollution prevention facility area (㎡) of sea level rise vulnerable areas A climate change vulnerability assessment method comprising a variable, an adaptation capacity variable including the GDP per capita (GRDP) (million won), the number of civil servants per population (persons / ten thousand), and the area of assistance facilities (㎡).

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