Assessing Eco-Efficiency in Asian and African Countries Using Stochastic Frontier Analysis

Energies

Mara Madaleno¹  Victor Moutinho² 

[1] GOVCOPP—Research Unit in Governance, Competitiveness and Public Policy, and Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;NECE—Research Center in Business Sciences and Management and Economics Department, University of Beira Interior, Portugal;

关键词: economic growth;    resource efficiency;    environmental efficiency;    Asian economies;    African economies;    efficiency scores;   

DOI  :  10.3390/en14041168

来源: DOAJ

【 摘 要 】

This study aims to evaluate the economic and environmental efficiency of Asian and African economies. In the model proposed, Gross Domestic Product (GDP) is considered as the desired output and Greenhouse Gases (GHG), like carbon dioxide (CO2) emissions, as the undesirable output. Capital, labor, fossil fuels, and renewable energy consumption are regarded as inputs, and the GDP/CO2 ratio is the output, by using a log-linear Translog production function and using data from 2005 until 2018, including 22 Asian and 22 African countries. Results evidence cross-countries heterogeneity among production inputs, namely labor, capital, and type of energy use and its efficiency. The models complement each other and are based on different distributional assumptions and estimation methods while providing a picture of Eco-efficiency in Asian and African economies. Labor and renewable energy share increase technical Eco-efficiency, while fixed capital decreases it under time-variant models. Technical improvements in Eco-efficiency are verified through time considering the time variable into the model estimations, replacing fossil fuels with renewable sources. An inverted U-shaped Eco-efficiency function is found concerning the share of fossil fuel consumption. Important policy implications are drawn from the results regarding the empirical results.

【 授权许可】

Unknown