Industrial structural transformation and carbon dioxide emissions in China
TL;DR: This article analyzed the relationship between industrial structural transformation and carbon dioxide emissions in China and found that the first-order lag of industrial structural adjustment effectively reduced the emissions; technical progress itself did not reduce the emissions, but indirectly led to decreasing emissions through upgrading and optimization of industrial structure.
About: This article is published in Energy Policy.The article was published on 2013-06-01. It has received 326 citations till now. The article focuses on the topics: Cleaner production.
Citations
More filters
01 May 1970
1,935 citations
TL;DR: Wang et al. as mentioned in this paper used a system generalized method of moments (SGMM) technique to estimate the effect of environmental innovation on carbon emissions in China and evaluated the effect on carbon emission reduction of China's initial carbon emissions trading (CET) scheme.
526 citations
Cites background from "Industrial structural transformatio..."
...…including CO2 emissions allowance allocation (Zhang et al., 2014; Zhang and Hao, 2015; Zhou and Wang, 2016), operational framework and mechanism (Zhou et al., 2013; Jiang et al., 2014; Xiong et al., 2015), market efficiency and economic impact (Zhao et al., 2015; Wang et al., 2016), as well as…...
[...]
TL;DR: In this article, the authors adopted the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimated the relationship using different panel date models.
Abstract: Urbanization and industrialization have significant impacts on energy consumption and CO2 emissions, but their relationship varies at different stages of economic development. Taking cognizance of heterogeneity and the “ratchet effect,” this paper adopts the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimates the relationship using different panel date models. The main results are obtained by dynamic panel threshold regression models, which divide a balanced panel dataset of 73 countries over the period of 1971–2010 into four groups according to their annual income levels. The key results are: (1) in the low-income group, urbanization decreases energy consumption but increases CO2 emissions; (2) in the middle-/low-income and high-income groups, industrialization decreases energy consumption but increases CO2 emissions, while urbanization significantly increases both energy consumption and CO2 emissions; (3) for the middle-/high-income group, urbanization does not significantly affect energy consumption, but does hinder the growth of emissions; while industrialization was found to have an insignificant impact on energy consumption and CO2 emissions; (4) from the population perspective, it produces positive effects on energy consumption, and also increases emissions except for the high-income group. These novel methodology and findings reveal that different development strategies of urbanization and industrialization should be pursued depending on the levels of income in a bid to conserve energy and reduce emissions.
496 citations
TL;DR: In this article, the authors investigated the causal linkage among CO2 emissions per capita, energy intensity, real GDP, industrialization, urbanization, and renewable energy consumption in China over the period from 1970 to 2015.
361 citations
TL;DR: In this paper, the authors examined the impacts of industrialization and urbanization on CO 2 emissions in China using nonparametric additive regression models and provincial panel data from 1990 to 2011.
340 citations
Cites background from "Industrial structural transformatio..."
...From the perspective of the overall industry, Li and Xia (2013) and Zhou et al. (2013a) concluded that industrialization was one of the most important factors affecting China's CO2 emissions....
[...]
References
More filters
TL;DR: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs and methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs.
25,433 citations
TL;DR: In this paper, a model of long run growth is proposed and examples of possible growth patterns are given. But the model does not consider the long run of the economy and does not take into account the characteristics of interest and wage rates.
Abstract: I. Introduction, 65. — II. A model of long-run growth, 66. — III. Possible growth patterns, 68. — IV. Examples, 73. — V. Behavior of interest and wage rates, 78. — VI. Extensions, 85. — VII. Qualifications, 91.
20,482 citations
TL;DR: In this paper, two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator are presented. But both estimators require restrictions on the initial conditions process.
19,132 citations
TL;DR: In this paper, a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables is presented. But the authors do not consider models with predetermined variables that have constant correlation with the effects.
16,245 citations
TL;DR: The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs as mentioned in this paper.
Abstract: In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale in multiple input and multiple output situations. The results are discussed and related not only to classical single output economics but also to more modern versions of economics which are identified with "contestable market theories."
14,941 citations