A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries

CO2 emissions reduction of Chinese light manufacturing industries: A novel RAM-based global Malmquist–Luenberger productivity index

An improved production-theoretical approach to decomposing carbon dioxide emissions

Environmental efficiency and economic growth of China: A Ray slack-based model analysis

Purpose Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model. Findings After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process. Originality/value The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

The measure proposed in this paper is a new nonparametric data envelopment analysis (DEA) scheme, the hybrid measure, for determining efficiency in the presence of radial and nonradial inputs or outputs. Further extension of the scheme occurred to address nonseparable desirable and undesirable outputs. Applying the model to measure the overall efficiency of U.S. electric utilities in the presence of both desirable and undesirable outputs indicated that the utilities had improved their overall management and environmental efficiency between 1996 and 2000. In accordance with global environmental conservation awareness, undesirable outputs of production and social activities (e.g., air pollutants and hazardous waste) have harmful social and environmental dimensions. Thus, development of technologies with less undesirable outputs is important in every area of production. Data envelopment analysis (DEA) usually indicates that producing more outputs relative to fewer input resources is a criterion of efficiency. In the presence of undesirable outputs, however, one should recognize technologies with more good (desirable) outputs and fewer bad (undesirable) outputs relative to fewer input resources as efficient. Addressing the problem included integrating the radial and nonradial measures of efficiency in DEA into a unified framework called the hybrid measure. The extension of the model followed to address desirable (good) and undesirable (bad) outputs where separable and nonseparable goods and bads in input and output items were evident. Conducting the empirical study involved applying the model to 30 U.S. electric utilities over five years (1996-2000) using two inputs, total generation capacity (separable) and fuel consumption (nonseparable), and four outputs, nonfossil power generation (separable good), fossil power generation (nonseparable good), nitrogen oxide emissions (nonseparable bad), and sulfur dioxide emissions (nonseparable bad). Reducing bad outputs is an important objective of the electric utilities but not their only goal. Utilities have to supply electricity to their customers, manage efficient production, and make a profit. The purpose of this study was to measure overall efficiency, taking into account not only environmental but also management efficiency. The results indicate that the U.S. utilities under study improved their overall management and environmental

PurposeThis paper aims to measure Chinese regional thermal industries’ evolution.Design/methodology/approachThis paper uses data envelopment analysis (DEA) and global Malmquist–Luenberger productivity (GMLP) index.FindingsThe results reveal that the development of Chinese thermal power industry varies significantly in different regions, and it is highly correlated with the level of local economic development. Although the change of technical efficiency and scale efficiency had different impacts on different regions from year to year, the overall GMLP index change shows a close relationship with the contemporaneous frontier shift.Practical implicationsThe results indicate that the Chinese Government should make efforts to promote its policy implementations and regulations in thermal industries so that the contemporaneous frontier will shift toward the global technology frontier with more desirable outputs and less undesirable outputs.Originality/valueAs an application, this study uses DEA and GMLP index to measure the productivity of Chinese thermal industries in 30 Chinese provinces from 2006 to 2013. The results have the meaningful policy implications for decision makers in charge of Chinese thermal industries.

Green total factor productivity measurement of industrial enterprises in Zhejiang Province, China: A DEA model with undesirable output approach

A new inverse DEA model with frontier changes for analyzing the achievement path of CO2 emissions target of China in 2030

Elucidating the complex mechanism between urbanization, economic growth, carbon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997–2010, this study empirically examines the relationships among urbanization, economic growth and carbon dioxide (CO2) emissions at the national and regional levels using panel cointegration and vector error correction model and Granger causality tests. Results showed that urbanization, economic growth and CO2 emissions are integrated of order one. Urbanization contributes to economic growth, both of which increase CO2 emissions in China and its eastern, central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central regions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization, economic growth and CO2 emissions, indicating that in the long run, urbanization does have a causal effect on economic growth in China, both of which have causal effect on CO2 emissions. At the regional level, we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run, we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped relationship between CO2 emissions and economic growth in China, not supporting the environmental Kuznets curve (EKC) hypothesis. Our empirical findings have an important reference value for policy-makers in formulating effective energy saving and emission reduction strategies for China.

This paper aims to address the problem of allocating the CO2 emissions quota set by government goal in Chinese manufacturing industries to different Chinese regions. The CO2 emission reduction is conducted in a three-stage phases. The first stage is to obtain the total amount CO2 emission reduction from the Chinese government goal as our total CO2 emission quota to reduce. The second stage is to allocate the reduction quota to different two-digit level manufacturing industries in China. The third stage is to further allocate the reduction quota for each industry into different provinces. A new inverse data envelopment analysis (InvDEA) model is developed to achieve our goal to allocate CO2 emission quota under several assumptions. At last, we obtain the empirical results based on the real data from Chinese manufacturing industries.

DEA radial measurement for environmental assessment: A comparative study between Japanese chemical and pharmaceutical firms

Purpose – The conducted research aimed at estimating the technical efficiency of didactic activity in the group of universities of technology in Poland using the non-parametric Data Envelopment Analysis (DEA) method. Research methodology – The measurement was based on the model of directional distance function with undesirable output under variable return-to-scale and output-oriented (DDF BadOutput-V-O). Findings – The research allowed to group universities in 2010 and 2015 into three categories, i.e. efficient universities and universities above and below the average efficiency. It has been shown that it is justified to use alternative models of efficiency measurement covering different perspectives. The application of the model from a financial and employment perspective showed significant differences in performance levels in some cases. Research limitations – This study only looks at universities of technology, so the future study should be extended to other universities and compare efficiency of higher education with the level of study effectiveness (dropout rate education, graduation rates). Practical implications – The use of the DDF model with undesirable output allowed to obtain results closer to the actual conditions of teaching in public universities than in the case of using classic DEA models, wich only take the desired output into accoount. Originality/Value – The originality of the work lies in the use of a more general and flexible DDF approach than the classical DEA models, which made it possible to estimate the efficiency of universities taking into account the desirable (positive) and undesirable (negative) output.

Most authors apply the Granger causality-VECM (vector error correction model), and Toda–Yamamoto procedures to investigate the relationships among fossil fuel consumption, emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, emissions, and fossil fuel consumption in China from 1965–2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.

An integrated environmental and operational evaluation model is constructed by data envelopment analysis (DEA) to examine seven thermal power plants operating in Taiwan during 2001–2008. Inputs and desirable outputs along with undesirable outputs, including CO2, SOx, and NOx emissions, were simulated. A slack variable analysis was conducted to identify possible ways to improve the inefficient power plants. In addition, three models were compared to identify the actual magnitude of inefficiency. The results indicate that the integrated efficiency and production scale of some plants were inefficient during 2001–2008. Reductions in fuel consumption and CO2 emissions are identified as the major strategies to improve efficiency. Other options include modifying pre-existing management measures, installing pollution prevention controls and resizing the scale of the power plant. It is anticipated that the findings of this study will help policymakers to achieve better environmental and operational performance with regard to existing thermal power plants.

An integrated approach of discrete event simulation and a non-radial super efficiency data envelopment analysis for performance evaluation of an emergency department