Integrated assessment of China’s adaptive capacityto climate change with a capital approach

Minpeng Chen & Fu Sun & Pam Berry & Rob Tinch &

Hui Ju & Erda Lin

Received: 20 December 2013 /Accepted: 26 May 2014# Springer Science+Business Media Dordrecht 2014

1 Introduction

Although many impacts of climate change are difficult to discern due to successful adaptationand the influence of non-climatic drivers, the effects of climate change, particularly temper-ature rises, in various socio-economic systems have been increasingly documented (Parry2007). Even if atmospheric greenhouse gas (GHG) concentrations were to remain at 2000levels, past emissions are estimated to cause some unavoidable warming (Solomon et al.2007). In reality, however, GHG emissions and concentrations continue to rise, locking us intofurther long-term climate change. Therefore improving adaptive capacity to deal with climaticvariability and climate-related disasters is imperative for socio-economic systems that areincreasingly challenged by the economic, social, health and environmental consequences ofthese changes.

Climatic ChangeDOI 10.1007/s10584-014-1163-7

This article is part of a Special Issue on “Regional Integrated Assessment of Cross-sectoral Climate ChangeImpacts, Adaptation, and Vulnerability” with Guest Editors Paula A. Harrison and Pam M. Berry

Electronic supplementary material The online version of this article (doi:10.1007/s10584-014-1163-7)contains supplementary material, which is available to authorized users.

M. Chen :H. Ju : E. LinInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of AgriculturalSciences (CAAS), Beijing 100081, People’s Republic of China

M. Chene-mail: chenmp@ami.ac.cn

M. Chen :H. JuKey Laboratory for Agricultural Environment, Ministry of Agriculture, Beijing 100081, China

F. Sun (*)School of Environment, Tsinghua University, Beijing 100084, Chinae-mail: sunfu@tsinghua.edu.cn

P. BerryEnvironmental Change Institute, Oxford University Centre for the Environment, Oxford, UK

R. TinchIodine SPRL, Brussels, Belgium

In China, climate change is likely to have wide-ranging impacts on almost all sectors ofsocio-economic systems, and adaptation to climate change and adaptive capacity building willbecome important tasks for China to cope with climate change, as well as promote sustainablesocio-economic development in the coming decades (Information Office of the State Council2011). Adaptation could be integrated with development priorities in some cases (e.g. climate-proof urban planning), while in others (e.g. coastal flood protection) adaptation would competewith other development needs for capital investment, especially in developing regions wherecapital resources are usually limited. To this end, China needs to preferentially direct capitalinvestment towards the most vulnerable areas and sectors, which are most affected by climatechange, or have the poorest adaptive capacities, or both. This study specifically focused on theadaptive capacity of China at a national level. To the authors’ knowledge, however, there arenot yet any studies which systematically assess China’s adaptive capacity to climate changeand identify critical regions so as to support sound adaptation policymaking.

Over the last decades, a number of concepts have been developed for adaptive capacity inorder to set the theoretical and scientific frame for relevant assessments. The majority of theseoriginated as components within the vulnerability framework and were vaguely defined,making it difficult to distinguish adaptive capacity from sensitivity (Adger 2006; Chapin IIIet al. 2009; Metzger et al. 2006; Parry 2007; Schneiderbauer et al. 2013; Smit and Wandel2006; Turner et al. 2003; Yohe and Tol 2002). In practice, adaptive capacity assessmentusually begins with identification of its determinants, which constitute the skeleton of anassessment framework. Determinants that have been discussed include economic wealth,technology and infrastructure, information, knowledge and skills, institutions, equity, andsocial capacity (Engle and Lemos 2010; Hill and Engle 2013; Kuhlicke et al. 2011; Metzgeret al. 2005). With these concepts and framework, there has been a growing body of literatureon proxy indicators of adaptive capacity in various social-economic systems at different spatialscales (Folke et al. 2005), e.g. at a national scale (Brooks et al. 2005), a regional scale(Goldman and Riosmena 2013; Hill 2012), and a river-basin scale (Heikkila et al. 2013;Pandey et al. 2011). However, the majority of current quantitative studies on adaptive capacityassessment are either case-specific or context-specific or too general for contextualization.Compared with its vital importance, adaptive capacity is a “relatively under-researched topic”within the global change community as well as the sustainability science (Engle 2011).

In the CLIMSAVE project, adaptive capacity was regarded as determined by wealth andreflected through capital stocks, which enables an understanding of adaptive capacity as tied tosocio-economic entitlements and asset bundles. The project also quantified adaptive capacityas a simple function of engineering (or manufactured or produced), natural, human, social andfinancial capital (Tinch 2010). This paper adopted the core methodology of CLIMSAVE toestablish an integrated framework for quantifying China’s adaptive capacity to climaticvariability and climate-related disasters, both at a national level and in a regionally explicitway, and identify policy and management options for building adaptive capacity.

2 Methods and data

2.1 Assessment approach and framework of adaptive capacity

The capital approach, also known as the theory of total national wealth, was developed tomeasure the sustainability of a defined geographic area, such a country, a region, etc. (Stern1995). This approach considers all tangible and intangible capital giving rise to consumptionpossibilities or well-being and comprises of financial, engineering, natural, human and social

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capital components (UNECE 2009). In the review by Tinch (2010), the capital approach isproposed as a bottom-up assessment framework applicable to adaptive capacity. Similarstudies also argue that adaptive capacity depends on the collective action within the suite ofenvironmental, social, economic and political entitlements (Goldman and Riosmena 2013) andit could be defined as “the collective ability of a group (or community) to combine variousforms of capital” (Adger 2010; Pelling and High 2005; Williamson et al. 2012). Comparedwith other methods for adaptive capacity assessment (see Table S1 in supplementary infor-mation for details), the capital method has the advantages of measurability, data availability,and clear distinguishing between the similar concepts of adaptive capacity and coping capacity(Dunford et al., in review; Tinch et al., in review).

A three-tier framework adopting this capital approach was applied here to characterize thefive pillars (natural environment, material base and infrastructure, availability of financialresources, human resources, and social stability) determining adaptive capacity to climatechange and conduct integrated assessment of regional adaptive capacity. As shown in Fig. 1,adaptive capacity was assessed on the basis of the following five supporting components.

(1) Natural capital (N) refers to natural resources and ecological systems that provide goodsor services necessary for adaptation activities for climate change, including availablefreshwater, arable land and ecological buffer zones.

Fig. 1 Framework for adaptive capacity assessment, adapted from Graedel et al. (2012)

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(2) Engineering capital (E) refers to material goods, including tools, machines, buildings andinfrastructure, contributing to adaptation processes.

(3) Financial capital (F) describes the availability or potential of economic resources thatcould be used for adaptation to climate change, measured by an economic developmentindex and an investment capacity index.

(4) Human capital (H) reflects stock of economically productive human capabilities thatinfluence adaptation, including knowledge, skills, competencies, education, and labouravailability.

(5) Social capital (S) is the social structure, institution, networks and relationship that enableor disable individuals or communities to maintain and develop their adaptation behav-iours and actions, particularly in partnership with others.

For each capital component, several composite indices were established to represent thestock or flow of this type of capital, and altogether there were 17 indices for the five capitalcomponents. Again, each composite index was derived from several directly relevant andeasily accessible indicators that promote or constrain adaptive capacity, and in total 46indicators were chosen to calculate the 17 indices as shown in Table 1.

2.2 Quantification process and data sources

Before quantifying adaptive capacity, a normalization procedure was first applied to convert allthe indicators on different units and scales into dimensionless ones between 0 and 1 by settingminimum and maximum values for each indicator. The indicators in Table 1 could generally beclassified into positive and negative ones (Table S2). The former are those that contributepositively to regional adaptive capacity, and most indicators in Table 1 belong to this group.The latter could be regarded as constraints or limits on building adaptive capacity, such aspopulation in poverty and unemployment rate. Equation (1) and (2) show the normalizationmethods for positive and negative indicators respectively,

x∧ ¼ x−xminxmax−xmin

ð1Þ

x∧ ¼ xmax−xxmax−xmin

ð2Þ

where x and x∧ are the original and normalized data, respectively; and xmin and xmax are theminimum and maximum for each indicator, respectively.

After normalization, each individual indicator could be aggregated into a composite indexbased on the framework shown in Fig. 1 following Equation (3),

X̄ ¼X

i¼1

n

wix∧i ð3Þ

where X is the composite index; n is the number of indicators that make up theaggregated index; and w is the assigned weight of each indicator to reflect its priorityand importance in the index. Analytical hierarchy with expert consultation (Pandey et al.2011), fuzzy logic analysis (Acosta et al. 2013) and equal weighting (Milman et al. 2013;Schneiderbauer et al. 2013; Dunford et al., in review) have all been practiced to

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Table 1 Indicators, weights and data sources

Index Indicator Weights Data source

N1 Per capita freshwaterresources availability

1 China Water Statistical Yearbook(Ministry of Water Resources 2011)

N2 Percentage of naturalwetland coverage

0.25 China Statistical Yearbook(National Bureau of Statistics 2011)

China Urban Construction StatisticalYearbook (Ministry of Housing andUrban–rural Development 2011)

China Statistical Yearbookof Environment (National Bureauof Statistics and Ministry ofEnvironmental Protection 2011)

Percentage of forest land 0.25

Percentage of green spacein urban area

0.25

Percentage of nature reserves 0.25

N3 Per capita arable land area 1 China Statistical Yearbook(National Bureau of Statistics 2011)

E1 Coverage of water supplyin urban areas

0.5 China Statistical Yearbook(National Bureau of Statistics 2011)

China Health Statistical Yearbook(Ministry of Health 2011)

Percentage of populationwith improved sourcesof drinking water inrural areas

0.5

E2 Wastewater treatment ratein urban areas

0.167 China Urban Construction StatisticalYearbook (Ministry of Housingand Urban–rural Development 2011)

China Health Statistical Yearbook(Ministry of Health 2011)

Yearbook of China Water Resources(Editorial Board of CWR 2011)

Percentage of populationwith improved sanitationin rural areas

0.167

Percentage of areas withdrainage capacity inrural areas

0.167

Ratio of reservoir capacityto renewable surfacewater resources

0.167

Percentage of populationwith flood protectionin rural areas

0.167

Percentage of flood protectionagainst a 50-year flood

0.167

E3 Percentage of heating coverage 0.5 China Urban Construction StatisticalYearbook (Ministry of Housingand Urban–rural Development 2011)

Percentage of populationhaving access to gas

0.5

E4 Road network density 0.33 China Urban Construction StatisticalYearbook (Ministry of Housingand Urban–rural Development 2011);China Transportation andCommunication Yearbook(Ministry of Transport 2010)

Per capita publictransportation capacity

0.33

Transportation facilityindicator

0.33

E5 Number of agriculturalmachinery per arableland area

0.5 China Rural Statistical Yearbook(Rural Social and EconomicInvestigation Division and NationalBureau of Statistics 2011)Irrigation capacity per

arable land area0.5

F1 Per capita gross domesticproduction (GDP)

0.5 China Statistical Yearbook(National Bureau of Statistics 2011)

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determine the weights of indicators when aggregating them to develop similar ACIs.However, these weighting methods always involve subjectivity (Acosta et al. 2013;Schneiderbauer et al. 2013; Dunford et al., in review). Therefore equal weighting wasapplied here to all the indicators, indices and components to give a simple, preliminaryscreening level assessment as shown in Fig. 1 and Table 1.

Table 1 (continued)

Index Indicator Weights Data source

GDP growth rate 0.5

F2 Per capita public revenue 0.5 China Statistical Yearbook(National Bureau of Statistics 2011)Per capita public expenditure 0.5

H1 Social dependency ratio 0.33 The 6th National Population Census(Population Census Office andNational Bureau of Statistics 2012)

Life expectancy at birth 0.33

Natural populationgrowth rate

0.33

H2 Literacy rate 0.33 The 6th National Population Census(Population Census Office andNational Bureau of Statistics 2012)

Average years of education 0.33

Percentage of populationwith higher education

0.33

H3 Percentage of publicexpenditure on researchand development (R& D)in GDP

0.33 China Statistical Yearbook on Scienceand Technology (National Bureauof Statistics and Ministry of Scienceand Technology 2011); ChinaMeteorological Yearbook(Editorial Board of CMY 2011)

Annual number of patentsper 1,000 persons

0.33

Climate observationcapacity index

0.33

S1 Percentage of populationin poverty

0.33 China Statistical Yearbook(National Bureau of Statistics 2011)

Unemployment rate 0.33

Inflation rate 0.33

S2 Number of doctorsper 1,000 persons

0.33 China Statistical Yearbook(National Bureau of Statistics 2011)

Number of hospitalbeds per 1,000 persons

0.33

Per capita medicalaid expenditure

0.33

S3 Number of refrigeratorsper household

0.25 China Statistical Yearbook(National Bureau of Statistics 2011)

Number of air conditionersper household

0.25

Number of televisionsper household

0.25

Number of telephonesper household

0.25

S4 Coverage of basic healthinsurance in urban areas

0.5 China Statistical Yearbook(National Bureau of Statistics 2011)

Coverage of basic healthinsurance in rural areas

0.5

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The adaptive capacity index (ACI) was finally calculated, according to Equation (4), as anaggregate of the five adaptive capacity components with equal weights as shown in Fig. 1,

ACI ¼ wnN þ weE þ wf F þ whH þ wsS ð4Þ

where wn, we, wf, wh and ws are the weights of natural, engineering, financial, human andsocial capital, respectively.

To demonstrate the utility of the proposed methodology, it was applied to an integratedassessment of China’s adaptive capacity to climate change at the national level in the 2000s. Itis worth noting that adaptive capacity was regarded as a medium-term capacity in this study,and therefore it was evaluated as an average ACI during the 2000s based on the datasetsbetween 2001 and 2010. To account for regional disparity, the datasets for 31 provinces ofChina (excluding Hong Kong, Macao and Taiwan) were utilized to construct the “referencesystem” to gauge the differences in adaptive capacity among these regions. The country-leveldatasets of China, USA, UK and Australia were also measured against this “reference system”to evaluate the adaptive capacity of China as a whole and identify the gaps between China andthese developed countries.

The most widely accepted datasets in the public domain for China and the relevantcountries were used to calculate the ACIs, and detailed data sources are given in Table 1and Table S3.

2.3 Statistical analysis

Descriptive data analysis was performed for all the original indicators and outputs.Moreover, correlation analysis and cluster analysis were used to determine the relation-ships among different components of the ACI, derive the spatial pattern of provincialadaptive capacity, and identify the bottlenecks for building adaptive capacity in eachprovince. SPSS Statistics, version 19.0 (SPSS Inc., IBM Company) was utilized for theabove statistical analysis.

3 Results and discussion

3.1 ACIs in China

China, as a whole, had an ACI of 0.353 during the 2000s, which was lower than that of USA(0.619), UK (0.544) and Australia (0.608) with the same reference system. However, as shownin Fig. 2a, high regional disparity in ACIs existed among its 31 provincial regions with thehighest in Beijing (0.645) and the lowest in Guizhou (0.229). The distribution of provincialACIs in Fig. 2a was positively skewed and 17 out of 31 provinces had ACIs below the nationallevel, which affirms that it is not the extremely low adaptive capacity in just several provinces,but the general inadequacy of adaptive capacity among the majority of the provinces that hasresulted in China’s overall poor adaptive capacity.

As shown in Fig. 2b, the five components of China’s ACI were not equally developed,and their imbalanced progress could generally reflect China’s current status. The rela-tively higher scores of human, engineering and social capital indicated the great efforts

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Fig. 2 National and provincial ACIs of China in the 2000s. a Frequency distribution of provincial ACIs; b Fivecomponents of the national ACI in comparison with three developed countries; c Frequency distribution of eachACI component at the provincial level

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the Chinese government has made in the past several decades to boost socio-economicdevelopment, build infrastructure, and improve education and people’s living standardsand well-being. However, as a developing country with the largest population in theworld, China’s per capita financial and natural resources are rather limited, which issuggested by the relatively lower scores for these capitals. There was also great vari-ability in the scores of these components among the 31 provinces (Fig. 2c). Eighteenprovinces had lower scores for natural and social capital than the national level, while thescores for engineering, financial and human capital were below the national level in 16,15 and 23 provinces respectively. These results again suggest a general deficiency in thecapital resources supporting China’s adaptive capacity.

Correlation analysis was conducted among the five components of the 31 provincialACIs, and the results summarized in Table 2. Natural capital was found to be signifi-cantly negatively correlated with the other components except financial capital, while theother four components were all positively correlated with each other. The result, on theone hand, shows the efforts of the provinces to cope with scarce resources, throughcomprehensively building strong social coping capabilities, e.g. engineering measures(such as improving water use efficiency and wastewater treatment), to compensate for thedeficit of natural endowment and ease its limitation on socio-economic development,which, however, could in turn exacerbate the pressure on natural resources. On the otherhand, the result indicates that some regions, e.g. Tibet, are endowed with relativelyabundant natural resources but underdeveloped due to, amongst others, geographical andecological reasons.

3.2 Regional characteristics of ACIs in China

Figure 3 presents the spatial distribution of regional adaptive capacity of China based on theprovincial ACIs. It maps the 31 provinces into 5 groups according to the 20th 40th, 60th and80th percentiles of their ACIs, i.e. provinces with very high (0.402~0.645), high (0.367~0.402), medium (0.336~0.367), low (0.327~0.336), and very low (0.229~0.327) adaptivecapacity respectively. Figure 3 indicates a certain level of spatial clustering effects in theregional adaptive capacity in China. Shanghai, Jiangsu and Zhejiang provinces in the YangtzeRiver Delta region, for example, which constitute the largest economic zone in China, all hadvery high adaptive capacity to climate change, while the traditional industrial bases inNortheast China, i.e. Heilongjiang, Liaoning, and Jilin provinces, all had very high or highadaptive capacity. The provinces in the central region, such as Hubei, Shaanxi, Hunan, Henan,and Anhui, generally had medium or low adaptive capacity below the national average,

Table 2 Correlation among different components of provincial ACIs in China

Component Natural capital Engineering capital Financial capital Human capital Social capital

Natural capital 1.000 −0.684** −0.248 −0.689** −0.606**

Engineering capital 0.000 1.000 0.636** 0.914** 0.874**

Financial capital 0.178 0.000 1.000 0.743** 0.789**

Human capital 0.000 0.000 0.000 1.000 0.945**

Social capital 0.000 0.000 0.000 0.000 1.000

The upper triangular elements represent the correlation coefficients between the components listed in thelines and those in the columns, and the lower triangular elements are the p-values of these correlations. *:p<0.05 (2-tailed); **: p<0.01 (2-tailed)

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whereas Western China, especially the southwest region, e.g. Guangxi, Yunnan, Gansu, andGuizhou, had the lowest adaptive capacity.

As defined by Equation (4), an ACI is an aggregate index consisting of five components.Therefore, despite two provinces having similar ACIs, there may exist differences in theircomponents, which could suggest the relative strength and weakness of these components andhelp to identify priorities for enhancing adaptive capacity. Cluster analysis was thereforeperformed to categorize the 31 provincial ACIs based on the similarity in the profiles of theirfive components.

Three of the four municipalities directly under the jurisdiction of the central govern-ment, i.e. Beijing, Shanghai and Tianjin (Group A), had the highest ACIs and scores forengineering, financial, human and social capital in China, whereas their scores for naturalcapital were the lowest (Fig. 4a). As previously discussed, although the inadequacy ofnatural resources could, to some extent, be offset by vast investment in other socio-economic capital resources (e.g. Beijing’s plan to recycle 75 % of its wastewater by2015), this mode of expanding adaptive capacity is always accompanied by furtherpopulation growth and thus escalating pressure on natural resources. For Group Aprovinces, adaptive capacity building would rely not only on further improvement inthe efficiency of resource utilization and socio-economic development, but also on strict(er) control of the size of population in these megacities. The latter has been formalizedin China’s urbanization plan (2014–2020) released in March 2014.

In contrast to Group A which had the highest ACIs and lowest natural capital, InnerMongolia, Heilongjiang, Jilin, Xinjiang, and Tibet (Group B) were characterised by mediumto high ACIs as well as the top five scores for natural capital in China. Besides this commonstrength, as shown in Fig. 4b, engineering capital was the bottleneck for Inner Mongolia andTibet, while financial capital was the limiting component for Heilongjiang, Jilin and Xinjiang.Specifically, Tibet had the lowest scores in China for engineering capital (0.122) and humancapital (0.171). Therefore, in Group B provinces, harmonized development and conservationof natural resources, sustained economic growth and continuous investment in infrastructureshould be prioritized to enhance adaptive capacity.

Fig. 3 Spatial distribution of provincial ACIs in China

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Another group of seven provinces similar to Group B, including Hainan, Qinghai,Sichuan, Guangxi, Yunnan, Gansu, and Guizhou (Group C), also had the highest scoresof natural capital among the five components as shown in Fig. 4c, while their scoresfor engineering capital were not only lower than the other four components but alsoamong the lowest ten provinces in China. These seven provinces were also character-ized by low financial capital and their scores, except Qinghai, were the lowest or thesecond lowest among the five components. As a result, their ACIs were all below thenational average, and furthermore the latter five provinces had the national lowest. ForGroup C provinces, the same strategies as Group B could be applied to improveadaptive capacity.

Another group of provinces with high and very high ACIs, i.e. Zhejiang, Jiangsu,Liaoning, Shandong, and Guangdong (Group D), exhibited a relatively balanced profileof their capital resources except that, as shown in Fig. 4d, natural capital was thebottleneck for each province. Although resources problems, at the provincial level, arecurrently not as serious as for megacities, such as Beijing, it is concerning that theycould follow the trajectories of those megacities and finally encounter a severe deficit ofnatural resources, which will impair their adaptive capacity, since all these provinces liein the prime economic zones of China, i.e. Yangtze River Delta (Zhejiang and Jiangsu),Bohai Bay Rim (Liaoning and Shandong) and Pearl River Delta (Guangdong). Adaptivecapacity building in these provinces, therefore, demands harmonizing socio-economicdevelopment with the carrying capacities of their natural resources.

Fig. 4 ACI components of different groups of provinces based on cluster analysis

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Another two groups of provinces also had a relatively balanced structure of their fivecomponents, and the 11 provinces in these groups, except Shanxi, had medium or low adaptivecapacity. One group includes Fujian, Jiangxi, Shaanxi, Chongqing, and Ningxia (Group E),and as shown in Fig. 4e, their financial capital was the weakest or the second weakest amongthe five components and engineering capital was also relatively poor especially in Shaanxi,Chongqing and Ningxia. The situation of Group E was quite similar to Group C, whereas theirscores for natural capital were generally lower than those of Group C and just around thenational average. The other group includes Hebei, Hubei, Hunan, Henan, Anhui, and Shanxi(Group F), and their ACIs were all below the national average. For each province, as shown inFig. 4f, among the five components, only engineering capital had a score above or around thenational average, and furthermore the scores for natural and financial capital were consistentlythe lowest two. Considering the overall inadequacy in adaptive capacity in Group E and GroupF provinces, comprehensive measures need to be implemented for capacity building, which isa common strategy for other provinces with ACIs below the national average, i.e. Group C.However, these 11 provinces are more prone to the limitation of natural resources compared toGroup C, so the paths they take to enhance adaptive capacity are likely to shape the futureprofiles of their adaptive capacity. For example, they may resemble Group D if balancedprogress is made in socio-economic development and infrastructure construction, or Group Bwhen natural resources are much more treasured and preserved, or even Group A whenaggressive socio-economic development is intensified.

4 Conclusion

The capital approach has been applied to conduct an integrated assessment of regional adaptivecapacity of socio-economic systems to climatic variability and climate-related disasters. Theproposed three-tier framework quantified the ACI (the first tier) as a function of five capitalcomponents (the second tier), and each component was derived from a series of compositeindices (the third tier) which were further represented by easily available supporting indicators.The framework was applied to assess China’s adaptive capacity to climate change. China, as awhole, had relatively low adaptive capacity in the 2000s. There also existed a great disparity inACIs among the 31 provinces and, from a spatial perspective, clustering effects emerged witheastern provinces having relatively high ACIs, while central and western ones were generallylow. The 31 provinces could be grouped according to the profiles of their five components, anddifferent strategies for adaptive capacity building were accordingly proposed. The relation-ships among the five components determining the 31 provincial ACIs revealed that socio-economic development and investment in infrastructure and public utilities was able, to someextent, to compensate for the deficit of natural endowment and ease its limitation on adaptivecapacity.

From the perspective of methodology development, the approach could be tailored toother countries and regions, as well as sectors, by adapting specific indicators to theirsocio-economic statistical systems and choosing between different weighting methods.Scenario analysis, sensitivity analysis, and uncertainty analysis could also be introducedinto ACI assessment to provide foresight into the future and more robust and credibleresults for decision-making. From the perspective of application, the results of ACIassessment, alone, could help to identify priorities of policy intervention for adaptivecapacity building, while in combination with studies on climate change impact, they

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could give a comprehensive picture of the vulnerability to climate change in countries,regions and sectors.

Acknowledgments This study was funded by the National Basic Research Program of China(2012CB955904), the European Commission under the Seventh Framework Programme (CLIMSAVE Project,Contract No. 244031), and the National Natural Science Foundation of China (71103186).

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