How do environmental regulations affect carbon emission and energy efficiency patterns? A provincial-level analysis of Chinese energy-intensive industries

Climate change and efforts to mitigate it have given rise to an interest in the relationship between industry competitiveness, energy efficiency, and carbon emissions. A better understanding of this relationship can be essential for economic and environmental decision-makers. This paper presents empirical research evaluating industry competitiveness through the factors of energy efficiency and carbon emission in Europe’s most energy-intensive industries. The designed industry competitiveness measure index consists of seven components, grouped into three equally weighted sub-indexes: export performance, energy, and environmental. The export performance of the industry is described by the industry export growth rate, the share of the industry’s export, and the effects on the industry’s competitiveness of changes in a country’s export. The energy intensity of the industry and energy prices are integrated into the energy sub-index. The environmental sub-index consists of the industry’s emissions intensity, and the ratio of freely allocated allowances and verified emissions indicators. The findings indicate that countries with the highest index value also have a positive energy intensity and carbon emission indicator value. The average index value of each industry gradually reduces to zero, and the standard deviation of the index value shows a diminishing trend throughout all sectors, which implies that competitiveness in all sectors is increasing and that all countries are nearing the industry average. The ANOVA results show that: (1) the competitiveness index value was statistically significantly different in the investigated countries; (2) the competitiveness index value was statistically non-significantly different in the investigated industries; (3) there was a significant effect of the interaction between country and industry on the competitiveness index value. These results suggest that the country itself and industry/country interaction significantly affect the competitiveness index. However, it should be mentioned that industry per se does not substantially affect the competitiveness index score.

Download Full-text

How Does Environmental Regulations Effect Pattern of Carbon Emission and Energy Efficiency: A Provincial Level Analysis of Chinese Energy-Intensive Industries?

10.21203/rs.3.rs-443406/v1 ◽

2021 ◽

Author(s):

Thanh Quang Ngo

Keyword(s):

Energy Efficiency ◽

Panel Data ◽

Environmental Regulation ◽

Carbon Dioxide Emissions ◽

Environmental Regulations ◽

Dynamic Panel Data ◽

Green Energy ◽

Economic Implications ◽

The Impact ◽

Energy Intensive Industries

Abstract Energy has a huge environmental and economic implications in the modern community. Despite the rapid economic growth of China in the past two decades, it can further improve through sustainable green energy with more energy-efficient industries, so as to maintain a good balance between economic and social development. The performances of energy and carbon dioxide emissions are the critical indicators. On this basis, this work measures the impact of environmental regulations on energy efficiency based on 2008-17 panel data from 30 provinces in China. The total factor energy efficiency index (TFEEI) is calculated by the non-radial distance function (NDDF). In order to study the nonlinear relationship between environmental regulations and TFEEI, the dynamic threshold panel model is used under different environmental regulations, which can solve effectively endogenous problems and regional heterogeneity. The results show that, for energy-intensive industries, the overall average TFEEI level is still very low, with average values of 0.55 and 0.58, which are well below the ideal value (i.e., 1). Further, the dynamic panel data model findings showed a U-shaped significant relationship between China's TFEEI and environmental regulation. The findings reveal that environmental regulation effect on TFEI rises steadily as the values of Market-Based Environmental Regulations (MERs) and Command and control Environmental Regulations (CCERs) and surpass the corresponding thresholds. This research can help policymakers understand the effectiveness of various levels of environmental legislation to make more informed decisions.

Download Full-text

Fired Heater Preservation, a Systematic Approach for Operating Expenditure Optimization, Energy Efficiency and Carbon Emission Reduction

10.29118/ipa.50.17.126.f ◽

2018 ◽

Author(s):

Johanes Anton Witono

Keyword(s):

Energy Efficiency ◽

Emission Reduction ◽

Carbon Emission ◽

Systematic Approach ◽

Carbon Emission Reduction ◽

Operating Expenditure

Download Full-text

Determinants of Intimate Partner Violence in Pakistan: National and Provincial Level Analysis

Proceedings of the 3rd International Conference on Gender Research ◽

10.34190/igr.20.119 ◽

2020 ◽

Keyword(s):

Intimate Partner Violence ◽

Partner Violence ◽

Intimate Partner ◽

Provincial Level ◽

Level Analysis

Download Full-text

Green Campus Establishment Through Carbon Emission and Energy Efficiency Control

10.1109/icsgteis53426.2021.9650356 ◽

2021 ◽

Author(s):

Nundang Busaeri ◽

Nurul Hiron ◽

Ida Ayu Dwi Giriantari ◽

Wayan Gede Ariastina ◽

Ida Bagus Alit Swamardika

Keyword(s):

Energy Efficiency ◽

Carbon Emission ◽

Efficiency Control ◽

Green Campus

Download Full-text

Patterns of Protest in the People's Republic of China: A Provincial Level Analysis

Asian Affairs An American Review ◽

10.1080/00927678.2014.936799 ◽

2014 ◽

Vol 41 (3) ◽

pp. 91-107 ◽

Cited By ~ 3

Author(s):

Victor Cheung Yin Chan ◽

Jeremy Backstrom ◽

T. David Mason

Keyword(s):

People’S Republic Of China ◽

People's Republic Of China ◽

Republic Of China ◽

Provincial Level ◽

Level Analysis

Download Full-text

Modelling Carbon Emissions Efficiency from UK Higher Education Institutions Using Data Envelopment Analysis

Journal of Energy Research and Reviews ◽

10.9734/jenrr/2019/v3i330097 ◽

2019 ◽

pp. 1-18

Author(s):

Adefarati Oloruntoba ◽

Japhet Tomiwa Oladipo

Keyword(s):

Higher Education ◽

Energy Efficiency ◽

Data Envelopment Analysis ◽

Carbon Emission ◽

Scale Effects ◽

Higher Education Institutions ◽

High Energy ◽

Data Envelopment ◽

Emission Efficiency ◽

The Uk

Aims: To correlate the energy and carbon emission efficiency relative to research income, gross internal area, and population for all the Higher Education Institutions (HEIs) in the UK and to assess the comparative carbon emission efficiency of HEIs relative to economic metrics. Study Design: Analytical panel data study. Place and Duration of Study: This paper evaluates the energy efficiency of 131 HEIs in the UK subdivided into Russell and non-Russell groups from 2008 to 2015. Methodology: Data Envelopment Analysis (DEA) and Malmquist productivity indexes (MPI) are used for the efficiency calculations. Results: The empirical results indicate that UK HEIs have relatively high energy efficiency scores of 96.9% and 77.6% (CRS) and 98.5%, 86.3% (VRS) for Russell and non-Russell groups respectively. Conclusion: The evidence from this study reveals that HEIs are not significantly suffering from scale effects, hence, an increase in energy efficiency of these institutions is feasible with the present operating scale but would need to work on their technical improvements in energy use. Malmquist index analysis confirms the lack of substantial technological innovation, which impedes their energy efficiency and productivity gain. Findings show that pure technical efficiency accounts for the annual efficiency obtained in the DEA model, the technological progress in contrast is the source of their energy inefficiency.

Download Full-text

Total Factor Energy Efficiency, Carbon Emission Efficiency, and Technology Gap: Evidence from Sub-Industries of Anhui Province in China

Sustainability ◽

10.3390/su12041402 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1402 ◽

Cited By ~ 3

Author(s):

Ya Chen ◽

Wei Xu ◽

Qian Zhou ◽

Zhixiang Zhou

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Carbon Emission ◽

Energy Use ◽

Industrial Sector ◽

Policy Implications ◽

Light Industry ◽

Heavy Industry ◽

Technology Gap ◽

Emission Efficiency

The phenomena of “large energy consumption, high carbon emission, and serious environmental pollution” are against the goals of “low energy consumption, low emissions” in China’s industrial sector. The key to solving the problem lies in improving total factor energy efficiency (TFEE) and carbon emission efficiency (TFCE). Considering the heterogeneity of different sub-industries, this paper proposes a three-stage global meta-frontier slacks-based measure (GMSBM) method for measuring TFEE and TFCE, as well as the technology gap by combining meta-frontier technology with slacks-based measure (SBM) using data envelopment analysis (DEA). DEA can effectively avoid the situation where the technology gap ratio (TGR) is larger than unity. This paper uses the three-stage method to empirically analyze TFEE and TFCE of Anhui’s 38 industrial sub-industries in China from 2012 to 2016. The main findings are as follows: (1) Anhui’s industrial sector has low TFEE and TFCE, which has great potential for improvement. (2) TFEE and TFCE of light industry are lower than those of heavy industry under group-frontier, while they are higher than those of heavy industry under meta-frontier. There is a big gap in TFEE and TFCE among sub-industries of light industry. Narrowing the gap among different sub-industries of light industry is conducive to the overall improvement in TFEE and TFCE. (3) The TGR of light industry is significantly higher than that of heavy industry, indicating that there are sub-industries with the most advanced energy use and carbon emission technologies in light industry. And there is a bigger carbon-emitting technology gap in heavy industry, so it needs to encourage technology spillover from light industry to heavy industry. (4) The total performance loss of industrial sub-industries in Anhui mainly comes from management inefficiency, so it is necessary to improve management and operational ability. Based on the findings, some policy implications are proposed.

Download Full-text