Simultaneity modeling analysis of the environmental Kuznets curve hypothesis

Abstract

This paper provides an extensive survey of the great progress in the literature of energy-environment-growth nexus for both specific-and multi-county studies covering the period from 1978 to 2014. The survey focuses on country (ies) coverage, periods, modeling methodologies, and empirical conclusions. Our survey is based on the direction of causality between (i)energy consumption (electricity, nuclear, renewable and non-renewable) and economic growth; (ii) between economic growth and environment; and between the three variables at the same time. As a general remark from these studies is that the literature produced paradoxical and not conclusive results which energy consumption can boost economic growth through the productivity enhancement and it can boost also the environmental damages through the enhancement of pollutant emissions. This survey gives researchers a 'snap shot' of the literature on the causality between the four types of energy, environment and economic growth for both individual and collective cases. Understanding the causal links between environment, economic growth and different types of energy consumption provides a basis for discussion in order to design and implementating effective energy and environmental policies.

Impact of Economic Growth and Energy Consumption on Greenhouse Gas Emissions: Testing Environmental Curves Hypotheses on EU Countries

“…Other studies carried out involve Kuznets curve analysis on GHG/CO 2 , GDP, and energy consumption, using different methodologies, for different regions and states, such as Turkey [33], countries of the OECD [34][35][36][37], China [38], and Russia [39]. Some studies give a 'snap shot' of the literature related to this correlation for multiple countries [40], for 188 countries [41], for low-and middle-income countries [42], or for 106 countries classified into different income groups [43].…”

Greenhouse Gas Emissions, Energy Consumption and Economic Growth: A Panel Cointegration Analysis for 16 Asian Countries

“…These two variables are incorporated to permit the identification of an inverted-U-shaped relationship between the pollution indicator and GDP. Empirical studies published for different countries or regions are as follows: France (Iwata et al [ 36 ]), China (Zhang and Cheng [ 11 ]; Li et al [ 6 ]; Duan et al [ 37 ]), the United States of America (Plassmann and Khanna [ 38 ]), Malaysia (Lau et al [ 39 ]; Saboori and Sulaiman [ 40 ]), Tunisia (Fodha and Zaghdoud [ 41 ]; Farhani et al [ 21 ]), Vietnem (Al-mulai et al [ 42 ]), Indonesia (Sugiawan and Managi [ 43 ]), Turkey (Shahbza et al [ 44 ]; Yavuz [ 45 ]; Lean and Smyth [ 46 ]); Spain (Sephton and Mann [ 47 ]), 15 European countries (Apergis [ 19 ]), 47 African countries (Christopher et al [ 20 ]), 10 Middle Eastern and North African Countries (MENA; Farhani et al [ 21 ]), 17 OECD countries (Bilgili et al [ 22 ]; Cho et al [ 23 ]), 56 countries (Youssef et al [ 24 ]), 30 administrative regions in China (Li and Ma [ 48 ]), 21 Canadian industrial sectors (Hamit-Haggar [ 5 ]), 8 countries in Asia and Africa (Olubenga et al [ 25 ]), Central America (Apergis and Payne [ 13 ]), Latin America and Caribbean countries (Pablo-Romero and Jesús [ 49 ]).…”

“…To the best of our knowledge, there has thus far been no attempt to investigate the relationship between these variables within a trivariate framework across these 16 Asian countries. Previous published studies with regional sampling frames have posited that the relationship between pollutants, energy consumption and economic growth is described in terms of inverted-U functions, in which pollutants initially rise and then decline with respect to income (Apergis, [ 19 ]; Christopher et al [ 20 ]; Farhani et al [ 21 ]; Bilgili et al [ 22 ]; Cho et al [ 23 ]; Youssef et al [ 24 ]; Olubenga et al [ 25 ]) Environmental pollutants such as greenhouse gas emissions depend on the extent of economic development. Grubb et al [ 26 ] pointed out that developed economies more generally show divergences between GDP and emissions with no clear linkage between the former and the latter over the last two decades.…”

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

“…The air quality of the cities, of which the environmental carrying capacity exceeded or approached upper limit (78.4%), failed to reach the environmental standard, and according to the public survey, days with severe or even worse pollution degree accounted for 3.2% of the whole year. Environmental pollution has been seriously affecting our daily lives [ 1 , 2 ] and is expected to restrain economic development [ 3 ]. In recent years, the relationship between economic development and pollutant discharge has been widely researched.…”