中国生态福利绩效时空格局演变及其驱动效应分析

doi:10.18306/dlkxjz.2023.04.001

摘要/Abstract

摘要：

生态福利绩效是提升人类福祉的生态效率，分析生态福利绩效的时空格局与驱动效应有助于揭示中国人类福祉提升的生态效率地区差异及其原因。论文基于主客观综合福祉视角，对中国生态福利绩效的时空格局演变进行分析，运用对数平均迪氏指数法(LMDI)和随机前沿分析法(SFA)深入揭示中国生态福利绩效变化的驱动效应以及经济增长效应的决定因素。结果表明：① 2006—2018年中国生态福利绩效变化主要依赖经济增长效应驱动，但由于环境福祉效应与社会福祉效应的掣肘，中国生态福利绩效整体上从3.052降至2.641。在经济增长效应的决定因素中，技术进步发挥着最主要的促进作用。② 中国南方省份与北方省份生态福利绩效的平均值差距由2006年的0.762扩大至2018年的1.005，表现出“南高北低”的空间分异特征。南方地区的经济增长效应值远高于北方地区，是中国生态福利绩效南北差异扩大的主要原因。③ 中国生态福利绩效变化整体显现为经济增长驱动模式，仅北京、天津、上海为“经济增长+环境福祉改善”驱动模式。南北区域及东中西三大区域经济增长效应的决定因素存在明显的区域异质性。研究结果可为推进中国生态福利绩效区域协调发展提供参考建议。

关键词: 生态福利绩效, 综合福祉指数, 生态足迹, LMDI方法, SFA方法

Abstract:

Ecological well-being performance (EWP) is the ecological efficiency of improving human well-being. Analyzing the spatial and temporal patterns and driving effects of EWP can help reveal regional differences in ecological efficiency of human well-being enhancement in China and its causes. This study analyzed the evolution of the spatiotemporal patterns of EWP in China based on a comprehensive subjective and objective well-being perspective, and used the Logarithmic Mean Divisia Index (LMDI) method and Stochastic Frontier Analysis (SFA) method to reveal the driving effects of EWP changes and the determinants of economic growth effect in China. The results show that: 1) The changes in EWP in China in 2006-2018 relied mainly on the effect of economic growth, but the overall level of EWP decreased due to the constraints of environmental well-being effect and social well-being effect. Among the determinants of the economic growth effect, technological progress played the most important role. 2) The differences between the mean values of EWP of southern and northern provinces in China had increased from 0.762 to 1.005, showing a spatial variation of high in the south and low in the north. The economic growth effect value in the south was much higher than that in the north, which was the main reason for the widening of the north-south differences in EWP in China. 3) The change in EWP in China as a whole appeared to be driven by economic growth, with only Beijing, Tianjin, and Shanghai showing a change driven by economic growth + environmental well-being improvement. There was a significant regional heterogeneity in the determinants of the economic growth effect in the southern and northern regions and in eastern, central, and western China. The results of the study can provide reference suggestions for promoting the coordinated regional development of EWP in China.

Key words: ecological well-being performance, comprehensive well-being index, ecological footprint, LMDI method, SFA method

王圣云, 段练成. 中国生态福利绩效时空格局演变及其驱动效应分析[J]. 地理科学进展, 2023, 42(4): 617-628.

WANG Shengyun, DUAN Liancheng. Evolution of spatio-temporal patterns of ecological well-being performance in China and its driving effects[J]. PROGRESS IN GEOGRAPHY, 2023, 42(4): 617-628.

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变量	全国样本	南方地区	北方地区	东部地区	中部地区	西部地区
β₀	-0.690^***(0.065)	-0.598^***(0.109)	1.582^***(0.243)	-1.083^***(0.109)	-0.091(0.174)	-0.743^***(0.080)
ln K	1.036^***(0.013)	1.000^***(0.019)	0.625^***(0.035)	1.065^***(0.021)	0.753^***(0.051)	0.999^***(0.016)
ln L	0.031^**(0.015)	0.050^**(0.021)	0.436^***(0.040)	-0.002(0.022)	0.302^***(0.074)	0.058^**(0.023)
ln EF	-0.156^***(0.027)	-0.140^***(0.050)	0.228^***(0.032)	-0.366^***(0.054)	-0.177^***(0.050)	-0.170^***(0.027)
δ₀	-2.039^***(0.371)	-0.348(0.301)	0.789^***(0.257)	-2.633^***(0.433)	1.564^***(0.377)	-0.896(0.573)
City	0.034^***(0.005)	0.008(0.005)	0.008^***(0.003)	0.040^***(0.006)	-0.023^***(0.006)	0.014^***(0.005)
Encs	0.004^**(0.002)	-0.001(0.002)	0.003^***(0.001)	0.014^***(0.002)	-0.014^***(0.003)	-0.005(0.004)
Tec	-0.034^***(0.005)	-0.029^***(0.006)	-0.023^***(0.002)	-0.013^**(0.005)	-0.055^***(0.009)	-0.040^***(0.010)
Indus	0.021^***(0.005)	0.013^***(0.005)	0.002(0.002)	0.010^*(0.006)	0.025^***(0.004)	0.023^**(0.010)
Open	-0.008^***(0.002)	-0.004^***(0.001)	-0.002^**(0.001)	-0.009^***(0.001)	0.009^**(0.004)	-0.013^***(0.005)
Fdi	-0.003^***(0.001)	-0.002^**(0.001)	0.001(0.001)	-0.001(0.001)	0.001(0.002)	0.004^**(0.002)
σ²	0.059^***(0.009)	0.024^***(0.003)	0.011^***(0.001)	0.025^***(0.004)	0.014^***(0.002)	0.036^***(0.008)
γ	0.913^***(0.024)	0.839^***(0.031)	0.999^***(0.041)	0.766^***(0.043)	0.832^***(0.249)	0.978^***(0.016)
LLF	172.5	177.9	165.2	122.3	91.9	111.3
LR检验	220.7	155.1	259.6	125.3	122.4	89.2