Spatio-temporal evolution and driving mechanism of green economic efficiency at county level in the Yellow River Delta, China

doi:10.13287/j.1001-9332.202109.039

Abstract

Abstract: A comprehensive measurement of green economic efficiency in the Yellow River Delta region can provide a reference basis for achieving ecological protection and high-quality development. We constructed an evaluation system of green economic efficiency in counties based on multi-source remote sensing data. We adopted the Super-EBM model that considered non-expected output to make a comprehensive measurement of green economic efficiency in Yellow River Delta counties, used the kernel density function estimation method to portray the characteristics of spatial and temporal evolution, and finally used the system generalized moment estimation method to identify influen-cing factors. The results showed that the comprehensive efficiency and pure technical efficiency of the green economy in the Yellow River Delta counties showed a fluctuating upward trend from 2000 to 2015. The scale efficiency showed a rapid increase and then stayed stable in a ‘Γ' type trend, while the increase in comprehensive efficiency transformed from being driven by scale-technology to being led by technology. The comprehensive efficiency and pure technical efficiency of the green economy in the Yellow River Delta counties showed an evolutionary trend from ‘club convergence' to ‘overall convergence', with the low efficiency counties formed a ‘catch-up effect' on the high efficiency counties and the scale efficiency toward a balanced and smooth development. The comprehensive efficiency of green economy and its decomposition efficiency spatially formed a ‘mountain' pattern, which was high in the middle, low in the two wings, and the high value area concentrated in the headland of the Yellow River Delta and along the coast of Laizhou Bay. The high-value area showed the characteristics of a northwest-southeast shift, and the east and west wings of the Yellow River Delta formed low-value subsidence areas. Industrial structure, population concentration level, and fixed asset investment intensity had positive effects on green economic efficiency, while population urbanization rate had negative effects on green economic efficiency. There was an obvious ‘environmental Kuznets' effect between green economic efficiency and economic development level.

Key words: green economic efficiency, remote sensing data, spatial-temporal evolution, Super-EBM model, Yellow River Delta

CAO Nai-gang, ZHAO Lin, GAO Xiao-tong. Spatio-temporal evolution and driving mechanism of green economic efficiency at county level in the Yellow River Delta, China[J]. Chinese Journal of Applied Ecology, 2021, 32(9): 3299-3310.

References

[1] 朱美青, 史文娇, 黄宏胜. 江西省绿色发展区划. 应用生态学报, 2017, 28(8): 2687-2696 [Zhu M-Q, Shi W-J, Huang H-S. Green development regionalization in Jiangxi Province, China. Chinese Journal of Applied Ecology, 2017, 28(8): 2687-2696]
[2] 邓祥征, 金贵, 何书金, 等. 发展地理学研究进展与展望. 地理学报, 2020, 75(2): 226-239 [Deng X-Z, Jin G, He S-J, et al. Research progress and prospect on development geography. Acta Geographica Sinica, 2020, 75(2): 226-239]
[3] 习近平. 在黄河流域生态保护和高质量发展座谈会上的讲话. 求是, 2019(20): 1-5 [Xi J-P. Speech at the symposium on ecological protection and high quality development in the Yellow River Basin. Qiushi, 2019(20): 1-5]
[4] Lu X, Qu Y, Sun PL, et al. Green transition of cultivated land use in the Yellow River Basin: A perspective of green utilization efficiency evaluation. Land, 2020, 9: 475, doi: 10.3390/land9120475
[5] 李小建, 文玉钊, 李元征, 等. 黄河流域高质量发展: 人地协调与空间协调. 经济地理, 2020, 40(4): 1-10 [Li X-J, Wen Y-Z, Li Y-Z, et al. High-quality development of the Yellow River basin from a perspective of economic geography: Man-land and spatial coordination. Economic Geography, 2020, 40(4): 1-10]
[6] 曹靖, 张文忠. 不同时期城市创新投入对绿色经济效率的影响——以粤港澳大湾区为例. 地理研究, 2020, 39(9): 1987-1999 [Cao J, Zhang W-Z. The influence of urban innovation input on green economy efficiency in different periods: A case study of the Guangdong-Hong Kong-Macao Greater Bay Area. Geographical Research, 2020, 39(9): 1987-1999]
[7] Pearce D, Markandya A, Barbier E. Blueprint for a Green Economy: A Report. London: Earthscan Publications Ltd, 1989
[8] UNEP. Towards a Green Economy: Pathways to Sustaina-ble Development and Poverty Eradication: A Synthesis for Policy Makers[EB/OL]. (2011-02-21) [2021-06-03]. https://www.unep.org/greeneconomy
[9] Zhang H, Geng Z, Yin R, et al. Regional differences and convergence tendency of green development competi-tiveness in China. Journal of Cleaner Production, 2020, 254: 119922
[10] Mancini MS, Galli A, Niccolucci V, et al. Stocks and flows of natural capital: Implications for Ecological Footprint. Ecological Indicators, 2017, 77: 123-128
[11] 程钰, 王晶晶, 王亚平, 等. 中国绿色发展时空演变轨迹与影响机理研究. 地理研究, 2019, 38(11): 2745-2765 [Cheng Y, Wang J-J, Wang Y-P, et al. A compa-rative research of the spatial-temporal evolution track and influence mechanism of green development in China. Geographical Research, 2019, 38(11): 2745-2765]
[12] Tong C, Ding S, Wang B, et al. Assessing the targeta-vailability of China's investments for green growth using time series prediction. Physica A: Statistical Mechanics and its Applications, 2020, 537: 122724
[13] 辛龙, 孙慧, 王慧, 等. 基于地理探测器的绿色经济效率时空分异及驱动力研究. 中国人口·资源与环境, 2020, 30(9): 128-138 [Xin L, Sun H, Wang H, et al. Research on the spatial-temporal differentiation and driving force of green economic efficiency based on the geographic detector model. China Population, Resources and Environment, 2020, 30(9): 128-138]
[14] Tao XP, Wang P, Zhu BZ. Provincial green economic efficiency of China: A non-separable input-output SBM approach. Applied Energy, 2016, 171: 58-66
[15] 胡安军, 郭爱君, 钟方雷, 等. 高新技术产业集聚能够提高地区绿色经济效率吗? 中国人口·资源与环境, 2018, 28(9): 93-101 [Hu A-J, Guo A-J, Zhong F-L, et al. Can the high-tech industrial agglomeration improve the green economic efficiency of the region? China Population, Resources and Environment, 2018, 28(9): 93-101]
[16] 胡博伟, 周亮, 王中辉, 等. 干旱区资源型城市绿色经济效率时空分异特征. 资源科学, 2020, 42(2): 383-393 [Hu B-W, Zhou L, Wang Z-H, et al. Spatiotemporal differentiation of green economic efficiency of resource-based cities in arid area. Resources Science, 2020, 42(2): 383-393]
[17] Wang ZB, Wang XM, Liang LW. Green economic efficiency in the Yangtze River Delta: Spatiotemporal evolution and influencing factors. Ecosystem Health and Sustainability, 2019, 5: 20-35
[18] Tone K, Tsutsui M. An epsilon-based measure of efficiency in DEA: A third pole of technical efficiency. European Journal of Operational Research, 2010, 207:1554-1563
[19] Yuan W, Li J, Meng L, et al. Measuring the area green efficiency and the influencing factors in urban agglomera-tion. Journal of Cleaner Production, 2019, 241: 118092
[20] 赵林, 吴殿廷, 金芮合, 等. 中国省际绿色包容性效率的时空演变特征及其影响因素. 应用生态学报, 2019, 30(9): 3087-3096 [Zhao L, Wu D-T, Jin R-H, et al. Spatio-temporal evolution and influencing factors of inter-provincial green inclusive efficiency in China. Chinese Journal of Applied Ecology, 2019, 30(9): 3087-3096]
[21] 郭艳花, 梅林, 佟连军. 产业集聚对绿色发展效率的影响机制——以吉林省限制开发区为例. 地理科学, 2020, 40(9): 1484-1492 [Guo Y-H, Mei L, Tong L-J. The effect of industrial agglomeration on green develop-ment efficiency in restricted development zone of Jilin Province. Scientia Geographica Sinica, 2020, 40(9): 1484-1492]
[22] 林晓, 徐伟, 杨凡, 等. 东北老工业基地绿色经济效率的时空演变及影响机制——以辽宁省为例. 经济地理, 2017, 37(5): 125-132 [Lin X, Xu W, Yang F, et al. Spatio-temporal characteristics and driving forces of green economic efficiency in old industrial base of northeastern China: A case study of Liaoning Pro-vince. Economic Geography, 2017, 37(5): 125-132]
[23] 车磊, 白永平, 周亮, 等. 中国绿色发展效率的空间特征及溢出分析. 地理科学, 2018, 38(11): 1788-1798 [Che L, Bai Y-P, Zhou L, et al. Spatial pattern and spillover effects of green development efficiency in China. Scientia Geographica Sinica, 2018, 38(11): 1788-1798]
[24] 郭付友, 佟连军, 仇方道, 等. 鲁南经济带城乡绿色发展效率时空分异及驱动因素识别. 自然资源学报, 2020, 35(8): 1972-1985 [Guo F-Y, Tong L-J, Qiu F-D, et al. Spatio-temporal pattern and driving factors of urban-rural green development efficiency in Lunan Economic Belt. Journal of Natural Resources, 2020, 35(8): 1972-1985]
[25] Su S, Zhang F. Modeling the role of environmental regulations in regional green economy efficiency of China: Empirical evidence from super efficiency DEA-Tobit model. Journal of Environmental Management, 2020, 261: 110227
[26] 刘凯, 任建兰, 张宝雷. 黄河三角洲人地系统脆弱性演化特征及其影响因素. 经济地理, 2019, 39(6): 198-204 [Liu K, Ren J-L, Zhang B-L. Evolution cha-racteristics and influence factors of human-land system vulnerability in Yellow River Delta. Economic Geography, 2019, 39(6): 198-204]
[27] 许学工, 梁泽, 周鑫. 黄河三角洲陆海统筹可持续发展探讨. 资源科学, 2020, 42(3): 424-432 [Xu X-G, Liang Z, Zhou X. Land and sea coordination for sustainable development in the Yellow River Delta. Resources Science, 2020, 42(3): 424-432]
[28] 中华人民共和国发展和改革委员会. 黄河三角洲高效生态经济区发展规划[EB/OL]. (2009-12-23) [2021-06-03]. http://www.gov.cn/gzdt/2009-12/23/content_1494694.htm [National Development and Reform Commission. Development Plan of the Yellow River Delta High-efficiency Ecological Economic Zone EB/OL]. (2009-12-23) [2021-06-03]. http://www.gov.cn/gzdt/2009-12/23/content_1494694.htm]
[29] Andersen P, Petersen NC. A procedure for ranking efficient units in data envelopment analysis. Management Science, 1993, 39(10): 1261-1264
[30] 孙才志, 李欣. 基于核密度估计的中国海洋经济发展动态演变. 经济地理, 2015, 35(1): 96-103 [Sun C-Z, Li X. Dynamic evolution analysis of China's marine economy development based on kernel density estimation. Economic Geography, 2015, 35(1): 96-103]
[31] Arellano M, Bond S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 1991, 58: 277-297
[32] 任宇飞, 方创琳. 京津冀城市群县域尺度生态效率评价及空间格局分析. 地理科学进展, 2017, 36(1): 87-98 [Ren Y-F, Fang C-L. Spatial pattern and evaluation of eco-efficiency in counties of the Beijing-Tianjin-Hebei urban agglomeration. Progress in Geography, 2017, 36(1): 87-98]
[33] 东营市统计局. 东营统计年鉴. 北京: 中国统计出版社, 2001—2016 [Dongying Municipal Bureau of Statistics. Dongying Statistical Yearbook. Beijing: China Statistics Press, 2001-2016]
[34] 滨州市统计局. 滨州统计年鉴. 北京: 中国统计出版社, 2001—2016 [Binzhou Municipal Bureau of Statistics. Binzhou Statistical Yearbook. Beijing: China Statistics Press, 2001-2016]
[35] 潍坊市统计局. 潍坊统计年鉴. 北京: 中国统计出版社, 2001—2016 [Weifang Municipal Bureau of Statistics. Weifang Statistical Yearbook. Beijing: China Statistics Press, 2001-2016]
[36] 德州市统计局. 德州统计年鉴. 北京: 中国统计出版社, 2001—2016 [Dezhou Municipal Bureau of Statistics. Dezhou Statistical Yearbook. Beijing: China Statistics Press, 2001-2016]
[37] 淄博市统计局. 淄博统计年鉴. 北京: 中国统计出版社, 2001—2016 [Zibo Municipal Bureau of Statistics. Zibo Statistical Yearbook. Beijing: China Statistics Press, 2001-2016]
[38] 烟台市统计局. 烟台统计年鉴. 北京: 中国统计出版社, 2001—2016 [Yantai Municipal Bureau of Statistics. Yantai Statistical Yearbook. Beijing: China Statistics Press, 2001-2016]
[39] 程钰, 王亚平, 张玉泽, 等. 黄河三角洲地区人地关系演变趋势及其影响因素. 经济地理, 2017, 37(2): 83-89 [Cheng Y, Wang Y-P, Zhang Y-Z, et al. The evolution trend and driving factors of man-land relationship about the Yellow River delta area. Economic Geo-graphy, 2017, 37(2): 83-89]