Emergy-Based Regional Socio-Economic Metabolism Analysis: An Application of Data Envelopment Analysis and Decomposition Analysis

Abstract

Integrated analysis on socio-economic metabolism could provide a basis for understanding and optimizing regional sustainability. The paper conducted socio-economic metabolism analysis by means of the emergy accounting method coupled with data envelopment analysis and decomposition analysis techniques to assess the sustainability of Qingyang city and its eight sub-region system, as well as to identify the major driving factors of performance change during 2000-2007, to serve as the basis for future policy scenarios. The results indicate that Qingyang greatly depended on non-renewable emergy flows and feedback (purchased) emergy flows, except the two sub-regions, named Huanxian and Huachi, which highly depended on renewable emergy flow. Zhenyuan, Huanxian and Qingcheng were identified as being relatively emergy efficient, and the other five sub-regions have potential to reduce natural resource inputs and waste output to achieve the goal of efficiency.The results of decomposition analysis show that the economic growth, as well as the increased emergy yield ratio and population not accompanied by a sufficient increase of resource utilization efficiency are the main drivers of the unsustainable economic model in Qingyang and call for polices to promote the efficiency of resource utilization and to optimize natural resource use.

Levels of Technical, Allocative, and Groundwater Use Efficiency and the Factors Affecting the Allocative Efficiency of Wheat Farmers in Pakistan

“…There are two approaches used to estimate relative efficiency: (1) a parametric approach (e.g., Stochastic Frontier Approach (SFA)) and (2) a non-parametric approach (e.g., Data Envelopment Analysis (DEA)). Data envelopment analysis (DEA), is a non-parametric frontier approach to evaluate the relative efficiency of a set of homogeneous decision-making units (DMUs) featuring multiple inputs and outputs [32]. Using the DEA method, each farm's efficiency score is individually optimised through mono-objective linear programming, comparing resources used (inputs) and quantities produced (outputs) to the levels of other units [33].…”

Measuring Eco-Efficiency of Agriculture in China

“…Data envelopment analysis (DEA), which is a non-parametric frontier methodology, aimed at evaluating the relative efficiencies of a set of homogeneous decision-making units (DMUs) featuring multiple inputs and outputs by means of a variety of mathematical programming models, has long been serving as a methodology to evaluate economic, energy, environmental, and ecological efficiency [33][34][35]. As one of most popular DEA models, the slacks-based measure (SBM) model, which was proposed by Tone in 2001 [36,37], considers the input and output of each decision-making unit (DMUs) and provides an effective solution for the slack problem.…”

Prediction of CO2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

“…In 1994, York et al proposed the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model based on the IPAT [26,27]. The STIRPAT model preserves the multiplication structure of the IPAT model, taking population, wealth and technology as the key factors affecting the environment.…”