Eco-efficiency Convergence and Green Urban Growth in China

Abstract

Eco-efficiency measures if economic growth and environmental protection are effectively balanced. To understand the path of green urban growth in China, this article examines the converging patterns of eco-efficiency for 191 Chinese cities within 2003 and 2013. Two types of modified Data Envelopment Analysis (DEA) methods and spatial modeling approach are adopted in empirical analyses, with cities grouped based on three types of heterogeneities that facilitate the formation of potential convergence clubs. We find that major Chinese cities are β-converging in their eco-efficiency scores and are forming place-based convergence clubs in terms of geographical location, environmental policy, and resource endowments. Less efficient clubs are converging at a faster speed toward low-level steady states, while more efficient clubs are reaching separate high-level equilibria with relatively slow rates and longer half-life. We further raise corresponding policy implications that aim at retarding or reversing the ongoing trend of eco-efficiency deterioration.

Eco‐Efficiency Measurement of Green Buildings and Its Spatial and Temporal Differences Based on a Three‐Stage Superefficient SBM‐DEA Model

“…Yu et al [ 35 ] conducted an empirical examination of the eco-efficiency of key environmental protection prefecture-level cities in China from 2003 to 2015 to analyze the spatial and temporal differences in eco-efficiency of cities in different regions and provide insights for decision makers to quantitatively assess the sustainable development of cities. To explore the growth paths of green cities in China, Huang and Hua [ 36 ] used two improved DEA methods and spatial modelling to examine the convergence patterns of ecological efficiency in different heterogeneous cities, aiming to achieve an effective balance between economic growth and environmental protection in green cities. The causes of differences mainly refer to the influencing factors that cause spatial and temporal differences.…”

Clubs, Coase, and the Role of Government

“…One can imagine treating municipalities (or counties) as spatial clubs competing for members, thus incentivizing pollution control and inducing sorting according to preferences for clean air and water (e.g. Huang and Hua [2018]). Greenhouse gases are global public bads, i.e.…”

Land Finance, Producer Services Agglomeration, and Green Total Factor Productivity

“…At present, there are two main methods for measuring TFP: the parameter estimation method based on Solow’s (1957) residual value, and the nonparametric estimation method based on DEA, including, for example, the Malmquist index model and the slacks-based measure (SBM) model (Schatzer et al 2019). Since the directional SBM-bootstrap approach can reflect reality more effectively than other methods, this study draws on Huang and Hua’s (2018) model and uses the MaxDEA 7.0 software to calculate GTFP. The detailed calculation process is shown in Appendix A.…”