Dynamic relationship among environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China

2019 ◽  
Vol 144 ◽  
pp. 428-435 ◽  
Author(s):  
Xiongfeng Pan ◽  
Bowei Ai ◽  
Changyu Li ◽  
Xianyou Pan ◽  
Yaobo Yan
Keyword(s):  
Energy Efficiency ◽  
Panel Data ◽  
Environmental Regulation ◽  
Technological Innovation ◽  
Large Scale ◽  
Dynamic Relationship

scholarly journals Research on the Impact of Environmental Regulation and Technology Innovation on the Quality of Economic Growth

2021 ◽  
Author(s):  
Qingyang Wu
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Environmental Regulation ◽  
Technological Innovation ◽  
Environmental Regulations ◽  
Threshold Model ◽  
Fixed Effect Model ◽  
Paper Test ◽  
The Impact

Abstract：This paper uses the balanced panel data from 29 provinces (autonomous regions and municipalities) in China for a total of 17 years from 2000 to 2016 as a research sample, and establishes an empirical model to examine the impact of environmental regulations and technological innovation on the quality of economic growth. Then this paper test technological innovation as a threshold variable, in which play a regulatory role. Taking the provincial balanced panel data as a research sample, a fixed effect model, a system GMM model, and a panel threshold model were established for empirical testing and the robustness test. Based on the empirical results, this article draws the following conclusions: from a national perspective, environmental regulations and technological innovation can significantly promote the quality of economic growth; from a regional perspective, there are regional differences in impact effects. Under the constraints of environmental regulations, the promotion effect of technological innovation on the quality of economic growth will be reduced; the impact of environmental regulation on the quality of economic growth will have a "threshold effect", and environmental regulation can significantly promote the quality of economic growth only after crossing the threshold and the threshold of technological innovation.

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

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.

scholarly journals Threshold Effects of Environmental Regulation on Total Factor Energy Efficiency in China

2015 ◽  
Vol 8 (1) ◽  
pp. 33-37
Author(s):  
Ling Yun Huang ◽  
Hui Qiang Xie
Keyword(s):  
Energy Efficiency ◽  
Environmental Regulation ◽  
Technological Innovation ◽  
Industrial Structure ◽  
Threshold Value ◽  
Threshold Effects ◽  
Threshold Values ◽  
Positive Effects ◽  
Negative Effect ◽  
The Impact

This paper examines the threshold effects of environmental regulation on China’s total factor energy efficiency (TFEE) using technological innovation (as measured by patents) as a threshold variable. Using the Slacksbased measure-undesirable (SBM-undesirable) output model, we first estimate TFEEs in 30 Chinese provinces from 2000 to 2011 under the constraints of energy conservation and emissions reduction. We then analyze the impact of environmental regulation on TFEE based on the panel threshold regression model. The results show that the average TFEE in China from 2000 to 2011 is 0.503, indicating that this measure can be significantly improved. However, environmental regulation has threshold effects on TFEE. Stringent environmental regulation can only improve TFEEs in provinces with technological innovation levels between the first and second threshold values. When technological innovation levels are below the first or above the second threshold value, tighter environmental regulation would lower TFEE. The results suggest that environmental regulation does not always enhance TFEE and that the positive effect of environmental regulation on TFEE must fall within a range of threshold values. In addition, improving the technological innovation level and adjusting the industrial structure have positive effects on TFEE, while the irrational energy consumption structure has a negative effect on TFEE.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

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