Research on Spatial Differences and Driving Effects of Ecological Well-Being Performance in China

Abstract

The essential requirement of sustainable development is to maximize economic prosperity and well-being while remaining within natural boundaries. This study focused on three aspects. First, a unique ecological well-being performance (EWP) evaluation model was developed by combining subjective and objective well-being indicators to assess China’s EWP from 2006 to 2018. Second, the evolution of spatial differences in China’s EWP was examined using the Dagum Gini coefficient and four spatial polarization indicators, from the perspective of eight economic regions. Third, we used the Logarithmic Mean Divisia Index (LMDI) method to decompose the driving factors of China’s EWP into four effects: economic, technical, objective well-being, and subjective well-being. Effective ways to promote the coordinated and sustainable enhancement of EWP in China were determined. The results showed that the overall level of EWP in China decreased from 2006 to 2018. The growth rate of China’s residents’ happiness index was not only slightly slower than the growth rate of the human development index but also significantly slower than the ecological footprint index per capita. The spatial differences of EWP in China were found to be expanding. Inter-regional differences were found to be the primary source of spatial differences in China’s EWP. Meanwhile, the capacity for sustainable development among provinces was further stretched, and, thus, the spatial polarization of China’s EWP tended to deepen. The importance of economic growth in boosting EWP cannot be overstated. China must actively encourage scientific and technological innovation, transition to a green development model, and raise human well-being in tandem with economic development. This study contributes to a scientific foundation and is a valuable reference for long-term and coordinated regional development in China and other emerging countries.

Spatial Differences of Ecological Well-Being Performance in the Poyang Lake Area at the Local Level

“…The reason, on the one hand, is that this study included non-natural resource inputs in the indicator system and the other is that the technological and economic development level of the Poyang Lake area was relatively backward, which leads to low eco-economic efficiency. Like most cities, EWP in the Poyang Lake area is constrained by eco-economic efficiency, suggesting that China still needs to promote the green economic transformation to improve human well-being sustainably [ 12 ]. In addition, the measurement results of this study showed that using a two-stage Super-NSBM model is more accurate and reasonable than using the Super-SBM model, which considers undesired outputs from a single-stage perspective.…”

“…Zhu (2013) [ 11 ] introduced the concept of EWP in China and expressed it as the ratio of human development index (HDI) to ecological footprint (EF) that broadens its scope for development. In essence, EWP measures eco-efficiency in terms of enhancing well-being [ 12 ]. Therefore, EWP is an extension of sustainable development, broader than eco-efficiency.…”

“…In recent years, scholars have successfully conducted numerous sustainable development studies through EWP. They have mainly used two methods to evaluate EWP, namely, ratio and the efficiency model [ 12 ]. HDI [ 13 ] and life expectancy at birth (LEB) [ 14 ] are commonly used to measure objective well-being outputs and life satisfaction [ 15 ] as well as happiness index [ 16 ], commonly used to measure subjective well-being outputs in studies that apply the ratio method.…”

Spatio-Temporal Evolution and Influencing Factors of Ecological Well-Being Performance from the Perspective of Strong Sustainability: A Case Study of the Three Gorges Reservoir Area, China

“…Per-capita industrial energy consumption above the scale [46,48,54] Land resource consumption Built-up area per capita [42,46] Water consumption Water consumption per capita [48,55] Undesired output Environmental pollution water pollution Industrial wastewater emissions per capita [27,56] Air pollution Industrial waste gas emissions per capita, per-capita carbon emission [46,56] Land pollution Industrial solid waste generation per capita [24,56] Intermediate ). The China Carbon Accounting Database https://www.ceads.net.cn/ (accessed on 10 November 2022), the water resources bulletins of each district and county, and the China County Construction Statistical Yearbook were also used to acquire data on carbon emissions, water usage, and built-up area.…”

Using eco‐efficiency and eco‐wellbeing performance as indicators for urban sustainable development: A two‐stage network analysis

“…Wang, Zhang, and Yao (2021) and Zhou and Zhang (2021) used the super‐SBM model to estimate the EWP of 30 provinces in China. Wang et al (2022) combined subjective and objective well‐being indicators to evaluate EWP in China from 2006 to 2018. Their findings of both studies indicate that the average EWP in China shows a decreasing trend.…”