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Industrial structural transformation and carbon dioxide emissions in China

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TLDR
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.
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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.

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Citations
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Study on the industrial structure optimization under constraint of energy intensity

TL;DR: A multi-sector dynamic computable general equilibrium framework that integrates energy intensity module to explore the reverse feedback effect of energy intensity control on industry structure indicates that the tightening effect ofEnergy intensity constrains on the Industrial sector is most significant, followed by the Tertiary Industry.
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Heterogeneous low-carbon targets and energy structure optimization: Does stricter carbon regulation really matter?

TL;DR: In this article , the effect of carbon regulation on energy structure was investigated and it was shown that stricter carbon regulation has a negative effect on the energy structure and the negative effects become greater with the increase of carbon intensity reduction targets.
Journal ArticleDOI

Evaluation of the Effects of Urbanization on Carbon Emissions: The Transformative Role of Government Effectiveness

TL;DR: In this paper , the effects of urbanization on carbon emissions and the transformative role of government effectiveness were explored. And the results suggest an inverted U-shaped nexus between urbanization and carbon emissions, and for most OECD countries, urbanization is positive to increase carbon emissions.
Journal ArticleDOI

Industrial growth, clean energy generation, and pollution: evidence from top ten industrial countries

TL;DR: In this paper, the authors employed panel cointegration and Granger causation approaches to discuss the associations among carbon dioxide (CO2) emissions, GDP growth, clean energy generation, and industrial growth for the top ten industrial countries spanning the period 1980-2014.
Journal ArticleDOI

Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Lihui Zhang, +2 more
- 08 Jan 2019 - 
TL;DR: It is found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030, however, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.
References
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Journal ArticleDOI

Measuring the efficiency of decision making units

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.
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A Contribution to the Theory of Economic Growth

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.
Report SeriesDOI

Initial conditions and moment restrictions in dynamic panel data models

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.
Journal ArticleDOI

Another look at the instrumental variable estimation of error-components models

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.
Journal ArticleDOI

Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis

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.
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