A new quantitative model of ecological compensation based on ecosystem capital in Zhejiang Province, China

A new quantitative model of ecological compensation based on ecosystem capital in Zhejiang Province, China 1 2 3 first-author 1 2 3 corresp ‡Corresponding Author ‡Corresponding Author †E-mail:hjf@zju.edu.cn †E-mail:hjf@zju.edu.cn hjf@zju.edu.cn 1 2 Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310029, China Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310029, China Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province, Hangzhou 310029, China Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province, Hangzhou 310029, China Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310029, China Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310029, China Ecological compensation is becoming one of key and multidiscipline issues in the field of resources and environmental management. Considering the change relation between gross domestic product (GDP) and ecological capital (EC) based on remote sensing estimation, we construct a new quantitative estimate model for ecological compensation, using county as study unit, and determine standard value so as to evaluate ecological compensation from 2001 to 2004 in Zhejiang Province, China. Spatial differences of the ecological compensation were significant among all the counties or districts. This model fills up the gap in the field of quantitative evaluation of regional ecological compensation and provides a feasible way to reconcile the conflicts among benefits in the economic, social, and ecological sectors. Payment for ecosystem services (PES) Gross domestic product (GDP) Ecological capital (EC) Geospatial distribution INTRODUCTION 1 With the high-speed development of economy in China, the environmental pressure of resources doubles. As an important part and approach to the undertaking of regional ecological construction, ecological compensation is becoming one of the key and multi-discipline issues in the field of resources and environmental management (China Council for International Cooperation on Environment and Development, 2006 2006 ). It is called ecological compensation or eco-compensation in China, which has payment for ecosystem services (PES) or payment for ecological benefit (PEB). A great number of researches about ecological compensation have been made both in China and other countries, most of which are focused on forests (Skärbäck, 2007 2007 ; Murray and Abt, 2001 2001 ; Sierra and Russmana, 2006 2006 ), water (Becker, 1999 1999 ; Wang and Ma, 2002 2002 ; Zhang and Ruan, 2003 2003 ; Qin and Qiu, 2005 2005 ), and other systems. However, there are few researches on evaluating ecological compensation synthetically and quantitatively in large-scale areas. In this paper, we construct a new quantitative estimate model for ecological compensation, analyze the correlation between gross domestic product (GDP) and ecological capital (EC) based on remote sensing and using county as study unit, and determine standard value so as to evaluate ecological compensation from 2001 to 2004 in Zhejiang Province, China. The results can be useful in regional economic and social development. THEORY 1 Original data 2 Estimation model of ecological capital 2 Within a region, EC (yuan/m 2 ) is dynamic and varies with time. It is the total value of all ecosystem services and natural resources of all ecosystems, and it varies with types, areas, and qualities of ecosystems (Costanza et al., 1997 1997 ). Ecosystem services represent the benefits that living organisms derive from ecosystem functions that maintain the Earth’s life support system. The gross value of regional EC can be expressed as: EC = V POM + V AGR + V RNM + V HWR + V CSW + V WT , where V POM , V AGR , V RNM , V HWR , V CSW , and V WT (yuan/m 2 ) are the values of production of raw materials, regulation of atmospheric gas, recycling of nutrient matters, holding water resources, conservation of soil and water resources, and waste treatment, respectively. These values can be calculated by Eqs.( 2 2 )~( 8 8 ). $${V_{{\text{POM}}}} = NPP \times {R_{{\text{POM}}}}$$ , $${V_{{\text{AGR}}}} = NPP \times (1.62 \times {R_{{\text{C}}{{\text{O}}_2}}} + 1.2 \times {R_{{{\text{O}}_2}}})$$ , $${V_{{\text{RNM}}}} = NPP \times ({r_1}_{\text{N}} \times {r_2}_{\text{N}} \times {R_{\text{N}}} + {r_1}_{\text{P}} \times {r_2}_{\text{P}} \times {R_{\text{P}}} + {r_1}_{\text{K}} \times {r_2}_{\text{K}} \times {R_{\text{K}}})$$ , $${V_{{\text{HWR}}}} = {P_{{\text{mean}}}} \times {r_1}_{\text{w}} \times {r_2}_{\text{w}} \times {R_{\text{w}}}$$ , $$V_{CSW} = V_{RLA} + V_{LS} + V_{PSF}$$ , $${V_{{\text{WT}}}} = {V_{{\text{S}}{{\text{O}}_2}}} \times {V_{{\text{dust}}}}$$ , where net primary production ( NPP , g C/(m 2 ·a)) is the mass of raw materials produced per year , and R POM (yuan/g C) is the price of producing organic matter. R O 2 and R CO 2 (yuan/g C) are the prices of producing O 2 and CO 2 using industrial methods. r 1N is the distributive ratio of nitrogen in organic matter in corresponding ecosystem, r 2N is the ratio of pure nitrogen converted to nitrogenous fertilizer, and R N (yuan/g C) is the average price of nitrogenous fertilizer. The valuations of P and K are just as that of N. P mean (mm) is monthly precipitation, r 1w is the ratio between runoff precipitation and total precipitation, r 2w is the value ratio of decreasing runoff compared with bare land, and R w is the price of building reservoir. V RLA , V LS , and V PSF (yuan/g) are the values of reducing the land abandonment, lightening sedimentation, and protecting soil fertility; V SO 2 and V dust (yuan/g) are the values of removing SO 2 and dust. NPP means the estimated using an improved light utilization efficiency model (Sun and Zhu, 2000 2000 ), and can be modeled using the following formula: NPP = ε max × f 1 ( T )× f 2 ( β )× PAR × FPAR − RC , where PAR (MJ/m 2 ) is photosynthetical active radiation, ε max is the maximum efficiency of PAR , f 1 ( T ) and f 2 ( β ) account for the effects of air temperature and soil moisture on assimilation, and FPAR is the fraction of PAR absorbed by plant canopy, which can be determined by the normalized difference vegetation index (NDVI). RC (g C/(m 2 ·;month)) is the monthly amount of respirated carbon (Sellers et al., 1994 1994 ). Analysis of EC and GDP 2 We chose six representative counties (districts) (i.e., Hangzhou, Ningbo, Yiwu, Changxing, Taishun, and Longquan) based on economic condition in order to analyze the relationship between county ecosystem and economy. The average annual EC and GDP per area or per capita in these regions from 2001 to 2004 were shown in Table 1 1 . GDP: gross demostic product; EC: ecological capital 2 County name 2 Area (km 2 ) 2 GDP 2 EC 2 EC/GDP Per area (yuan/m 2 ) Per capita (×10 3 yuan) Per area (yuan/m 2 ) Per capita (×10 3 yuan) Hangzhou 683 149.70 52.51 1.65 0.58 0.01 Ningbo 1033 72.26 57.17 1.91 1.51 0.03 Yiwu 1103 16.16 26.19 1.96 3.18 0.12 Changxing 1428 7.21 16.61 1.85 4.26 0.26 Taishun 1762 0.89 8.35 4.53 42.48 5.09 Longquan 3059 0.75 8.35 3.43 38.08 4.56 The average values of county-level EC and GDP varied significantly in these regions both per area and per capita. The differences of EC and GDP per area varied much more significantly among the six regions than those per capita. EC/GDP ratio indicated that the ecological status and economical status were comparative: if the ratio ≥1, this region held certain ecological capacitance, while the ratio < l indicates that the regional ecological capacitance declined and that the ecological stress exceeded the respective biocapacity (Pan et al., 2004 2004 ). In other words, the counties supporting most ecosystem services were all poor regions or underdeveloped areas. There was a huge horizontal financial gap between economically developed counties and underdeveloped areas, and the gap increased with the passage of time. Estimate model of ecological compensation 2 Considering the financial system and the feasibility of implementing eco-compensation in Zhejiang Province, we chose county administration district as analysis unit in this study. According to the base year of 2001, we defined PES (yuan) as the county total value of ecological compensation. The formula of PES can be expressed as: PES = γ × PES p +(1− γ )× PES a , where PES p is the county total value of ecological compensation according to the population; PES a is the county total value of ecological compensation according to the region area. γ is the parameter characterizing the weights of PES p and PES a . In this study, we chose γ as 0.5, and the weight of population and area could be studied more specifically in the further research. Because of the different population densities in different regions, PES p can be calculated as: $$PE{S_{{\text{p}}(i,j)}} = \left[ {\left( {\frac{{E{C_{i,j + 1}} - GD{P_{i,j + 1}}}}{{T{P_{i,j + 1}}}} - \frac{{E{C_{i,j}} - GD{P_{i,j}}}}{{T{P_{i,j}}}}} \right) - \left( {\frac{{\sum\limits_{i = 1}^n {E{C_{i,j + 1}}} - \sum\limits_{i = 1}^n {GD{P_{i,j + 1}}} }}{{\sum\limits_{i = 1}^n {T{P_{i,j + 1}}} }} - \frac{{\sum\limits_{i = 1}^n {E{C_{i,j}}} - \sum\limits_{i = 1}^n {GD{P_{i,j}}} }}{{\sum\limits_{i = 1}^n {T{P_{i,j}}} }}} \right)} \right] \cdot T{P_{j + 1}}$$ , where EC i , j is the EC of the i th county in the j th year ( j =2001,2002,2003); GDP i , j is the GDP of the i th county in the j th year; TP i , j is the total population of the i th county in the j th year. PES i , j is the county total value of ecological compensation of the i th county from the j th year to the ( j+ 1)th year. Considering the area of every county administration districts, PES a can be calculated as: $$PE{S_{{\text{a}}(i,j)}} = \left[ {(E{C_{i,j + 1}} - GD{P_{i,j + 1}}) - (E{C_{i,j}} - GD{P_{i,j}})} \right] - \frac{{{S_i}\left[ {\left( {\sum\limits_{i = 1}^n {E{C_{i,j + 1}}} - \sum\limits_{i = 1}^n {GD{P_{i,j + 1}}} } \right) - \left( {\sum\limits_{i = 1}^n {E{C_{i,j}}} - \sum\limits_{i = 1}^n {GD{P_{i,j}}} } \right)} \right]}}{{\sum\limits_{i = 1}^n {{S_i}} }}$$ , where S i (km 2 ) is the area of the i th county (district). RESULTS 1 The spatial distribution of the county (district) total PES of Zhejiang Province is shown in Fig. 1 1 . Spatial differences of PES were significant among all the counties or districts. The county PESs in the northeast and southeast of Zhejiang Province were negative, while they were positive in west and southwest regions. The lowest values ( < −4.0 billion yuan) were found in municipal districts like Hangzhou, Xiaoshan, Ningbo, and so on. The rather low values (i.e., −4.0 to 0 billion yuan) were found in the north and east regions. The positive values were found in most of counties especially in the western Zhejiang. DISCUSSION 1 Majority of the important ecological function regions are coincident with the underdeveloped areas. For example, as the ecological barrier of Zhejiang Province, the southwestern counties are the underdeveloped areas. In an ecological viewpoint, these counties could obtain ecological compensation from other developed counties or municipal districts for required ecosystem services. In an economical viewpoint, they undertake more important mission to protect ecological environment than the economic developed areas like Hangzhou bay areas or coastal areas. What is more, as the river head of eight water systems in Zhejiang Province, they need to support the lower areas with clean water, which means resource-dependent industrial sectors (serious pollution industrial sectors) are limited in these regions. Generally speaking, PES can solve the problems and adjust the benefit distribution of ecosystem and economy between the relevant stakeholders. At the same time, it is necessary to reduce the income gap between the towns and the country, in order to boost the harmonious development of region, protect the resource and environment, realize the continuable development, and enhance the international competition. CONCLUSION 1 This model fills up the gap in the field of quantitative evaluation of regional ecological compensation based on ecological capital, and provides a feasible way to reconcile the conflicts among benefits in the economic, social, and ecological sectors. This study could be also considered as a supplement or necessary improvement to the research of existing ecological capital, as further theoretical research of ecological capital transfers in the conditions of different social economic development levels or different regional development. Considering the financial system and the feasibility of implementing eco-compensation, PES was evaluated in a unified administrative region. This model has universal properties, which can be applied in all scales of study area even in the world. It will help increase not only the governmental macro-regulating level, but also public consciousness of protecting environment. References N Becker . A comparative analysis of water price support versus drought compensation scheme . Agricultural Economics , 1999 . 21 ( 1 ): 81 - 92 . DOI: 10.1016/S0169-5150(99)00019-5 http://doi.org/10.1016/S0169-5150(99)00019-5 . 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