Environmental Efficiency and Its Determinants for Manufacturing in China

Abstract

Abstract:In this study, we analyze the environmental efficiency performance and its determinants of 29 manufacturing industries in China from 2006 to 2011 by employing a two-stage DEA (data envelopment analysis)-Tobit model. For providing comparative and robust evidence, the 29 manufacturing industries are classified into three groups based on the pollution intensity. In the first stage, a SBM (slacks-based measure)-DEA model is applied to assess economic efficiency and environmental efficiency scores to illustrate the effects of the environmental factors, while taking into consideration the undesirable output. In the second stage, utilizing these calculated environmental efficiency scores as dependent variables, we employ a Tobit regression model to study the determinants of the environmental efficiency by selecting three independent variables including the industry scale, the openness degree and the energy structure. It turns out that the environmental factors have a positive effect on the lightly polluted industries and a negative effect on the moderately polluted industries, the heavily polluted industries, and the overall industries, while the openness degree, the industry scale, and the energy structure can be effective measures to improve environmental efficiency. Based on these findings, we propose policy measures to enhance the environmental efficiency of manufacturing in China.

Sustainability Assessment of Taiwan’s Semiconductor Industry: A New Hybrid Model Using Combined Analytic Hierarchy Process and Two-Stage Additive Network Data Envelopment Analysis

“…This study follows previous works [12][13][14][15]25,27,[41][42][43] and assume that the first stage of the business growth process uses labour, operating expenses, and net fixed assets to produce sales, power consumption, and water consumption that serve as intermediate outputs. Labour is measured as the number of full-time employees.…”

“…Production (with pollutant byproducts called undesirable outputs) and pollution treated as a two-stage system have aroused increasing attention in the sustainability management field. Undesirable factors have been taken into account in measuring the efficiency of suppliers [24], eco-efficiency [25,26], and resource and environmental efficiency [15,27]. The literature has also presented DEA efficiency evaluation by considering undesirable factors, and undesirable factors can be regarded as inputs or undesired outputs in the DEA models [13,22,28].…”

“…Thus, the indicators in this context are necessary for policymaking [31]. While the previous studies have focused on development of environmental measures [27,32], this study propose a method that incorporates the financial and ecological aspects of sustainability. Figure 1 depicts the empirical framework of assessing process sustainability in this study.…”

“…The solutions of the environmental efficiency measures are complicated [27,41,42], especially for the cooperative ecological efficiency measure, because they represent non-linear programming problems [12,31]. The rational methods to address undesirable elements have been introduced into these environmental efficiency measures [13,22,28], and more reasonable and straightforward ecological efficiency measures have been investigated [47].…”

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

“…Masuda [112] utilized the SBM model in rice production. Wang et al [113] employed an SBM-DEA model in the manufacturing sector. Pang et al [86] integrated the directional distance function (DDF) and SBM to assess the total clean energy use of 86 countries.…”