Abstract
The ecological compensation mechanism is regarded as the direction for the future management of the ecological environment of the river basin, which has become a global concern. Ex-post assessments on the performance of ecological compensation programs contribute to further improvement and optimization in the process of exploration. This study proposes a novel performance assessment approach to address the issue of environmental efficiency evaluation with uncertainty by systematically integrating data envelopment analysis (DEA), bootstrap, regression, and exponential smoothing. The last two methods are used to fill in missing data, DEA super-SBM is applied to measure the performance, and bootstrap is adopted to stimulate more data. This approach is applied to performance measurement of Xin'an river basin in China. Validated by benchmark comparisons and statistical tests, the outcomes indicate that this integrated conceptual method can serve as an effective way to gauge environmental efficiency with ecological compensation when missing data are presented. The results obtained from the case highlight the limited positive effect of ecological compensation. Marginal utility brought about by the ecological compensation investment fund is declining, and the fund utilization in some projects also appears to be low. Consequently, suggestions for future optimization on fund and performance management, evaluation method and compensation mode are provided.
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References
Andreou, E., Ghysels, E., & Kourtellos, A. (2010). Regression models with mixed sampling frequencies. Journal of Econometrics, 158, 246–261.
Bergmeir, C., Hyndman, R. J., & Benitez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32, 303–312.
Chen, N., Xu, L., & Chen, Z. (2017). Environmental efficiency analysis of the Yangtze River economic zone using super efficiency data envelopment analysis (SEDEA) and tobit models. Energy, 134, 659–671.
Dong, F., Zhang, Y., & Zhang, X. (2020). Applying a data envelopment analysis game cross-efficiency model to examining regional ecological efficiency: Evidence from China. Journal of Cleaner Production 267: 122031.
Fu, Y., Zhang, J., Zhang, C., Zang, W., Guo, W., Qian, Z., Liu, L., Zhao, J., & Feng, J. (2018). Payments for Ecosystem Services for watershed water resource allocations. Journal of Hydrology, 556, 689–700.
Gardner, E. S., Jr. (2006). Exponential smoothing: The state of the art - Part II. International Journal of Forecasting, 22, 637–666.
Kosoy, N., Martinez-Tuna, M., Muradian, R., & Martinez-Alier, J. (2007). Payments for environmental services in watersheds: Insights from a comparative study of three cases in Central America. Ecological Economics, 61, 446–455.
Kourentzes, N., Petropoulos, F., & Trapero, J. R. (2014). Improving forecasting by estimating time series structural components across multiple frequencies. International Journal of Forecasting, 30, 291–302.
Lin, B., & Wang, X. (2014). Exploring energy efficiency in China’s iron and steel industry: A stochastic frontier approach. Energy Policy, 72, 87–96.
Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., & Khoshnoudi, M. (2017). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewable & Sustainable Energy Reviews, 70, 1298–1322.
Martin-Ortega, J., Ojea, E., & Roux, C. (2013). Payments for water ecosystem services in Latin America: A literature review and conceptual model. Ecosystem Services, 6, 122–132.
Moutinho, V., Madaleno, M., & Robaina, M. (2017). The economic and environmental efficiency assessment in EU cross-country: Evidence from DEA and quantile regression approach. Ecological Indicators, 78, 85–97.
PRC, & S.C.o.t. (2014). The environmental protection law of the People's Republic of China.
Rai, R. K., Nepal, M., Bhatta, L. D., Das, S., Khadayat, M. S., Somanathan, E., & Baral, K. (2019). ensuring water availability to water users through incentive payment for ecosystem services scheme: A case study in a small hilly town of Nepal. Water Economics and Policy, 05, 1850002.
Salzman, J., Bennett, G., Carroll, N., Goldstein, A., & Jenkins, M. (2018). The global status and trends of payments for ecosystem services. Nature Sustainability, 1, 136–144.
Schomers, S., & Matzdorf, B. (2013). Payments for ecosystem services: A review and comparison of developing and industrialized countries. Ecosystem Services, 6, 16–30.
Shang, W. X., Gong, Y. C., Wang, Z. J., & Stewardson, M. J. (2018). Eco-compensation in China: Theory, practices and suggestions for the future. Journal of Environmental Management, 210, 162–170.
Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44, 49–61.
Sims, K. R. E., & Alix-Garcia, J. M. (2017). Parks versus PES: Evaluating direct and incentive-based land conservation in Mexico. Journal of Environmental Economics and Management, 86, 8–28.
Sonter, L. J., Simmonds, J. S., Watson, J. E. M., Jones, J. P. G., Kiesecker, J. M., Costa, H. M., Bennun, L., Edwards, S., Grantham, H. S., Griffiths, V. F., Jones, K., Sochi, K., Puydarrieux, P., Quetier, F., Rainer, H., Rainey, H., Roe, D., Satar, M., Soares-Filho, B. S., … Maron, M. (2020). Local conditions and policy design determine whether ecological compensation can achieve No Net Loss goals. Nature Communications, 11, 2072.
Sueyoshi, T., Yuan, Y., & Goto, M. (2017). A literature study for DEA applied to energy and environment. Energy Economics, 62, 104–124.
Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509.
Tone, K. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143, 32–41.
Walker, N. L., Norton, A., Harris, I., Williams, A. P., & Styles, D. (2019). Economic and environmental efficiency of UK and Ireland water companies: Influence of exogenous factors and rurality. Journal of Environmental Management, 241, 363–373.
Zheng, H., Robinson, B. E., Liang, Y. C., Polasky, S., Ma, D. C., Wang, F. C., Ruckelshaus, M., Ouyang, Z. Y., & Daily, G. C. (2013). Benefits, costs, and livelihood implications of a regional payment for ecosystem service program. Proc Natl Acad Sci U S A, 110, 16681–16686.
Funding
This work was supported in part by the Technology and Innovation Major Project of the Ministry of Science and Technology of China under Grant 2020AAA0108400 and 2020AAA0108402, the Strategic Priority Research Program of CAS under Grant XDA23020203, the National Natural Science Foundation of China under Grant 71825007, the Chinese Academy of Sciences Frontier Scientific Research Key Project under Grant QYZDB-SSW-SYS021, and the International Partnership Program of Chinese Academy of Sciences, Grant No.211211KYSB20180042.
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Dong, J., Wu, D., Song, J. et al. Gauging the environmental efficiency with ecological compensation in presence of missing data using data envelopment analysis. Environ Dev Sustain 24, 5451–5472 (2022). https://doi.org/10.1007/s10668-021-01666-9
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DOI: https://doi.org/10.1007/s10668-021-01666-9