Assessing China’s Ecological Restoration Programs: What’s Been Done and What Remains to Be Done? |
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Authors: | Runsheng Yin Guiping Yin Lanying Li |
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Institution: | (1) Department of Forestry, Michigan State University, 126 Natural Resources Building, East Lansing, MI 48824, USA;(2) Ecosystem Policy Institute of China, Beijing, China;(3) School of Economics and Management, Zhejiang Forestry University, Lin’an, China |
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Abstract: | This article surveys the recent literature that has assessed China’s ecological restoration programs, including the Sloping
Land Conversion Program (SLCP) and the Natural Forest Protection Program (NFPP). Our presumption is that the performance of
these programs should be determined by their effectiveness of implementation and significance of impact. Implementation effectiveness
can be measured with such indicators as land area converted or conserved, and survival and stocking rates of restored vegetation,
while impact significance can be gauged by the induced changes in ecosystem functionality and stability (erosion control,
biodiversity protection, etc.) and socioeconomic conditions. Coupling this matrix with an exhaustive search of the publications,
we find that: (1) the implementation effectiveness has not been examined as extensively as the impact significance; (2) efforts
to assess the impact significance have concentrated on the SLCP, particularly its socioeconomic effects: growth of income,
alternative industry, and employment, and likelihood of re-conversion; and (3) most of the socioeconomic studies are based
on rural household surveys and discrete choice and difference in differences models. While much has been learned from previous
studies, a lot more needs to be done in improving our understanding of the program execution and impacts. Future work should
pay more attention to the NFPP and other programs, and the environmental impacts and the implementation effectiveness of all
of them. To these ends, analysts must gather more field data regarding the evolving ecosystem conditions and socioeconomic
information of higher aggregation, and conduct their research across scales and disciplines, with better application of geospatial
technology and more effective modeling. |
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