Institutional quality, employment, FDI and environmental degradation in developing countries: evidence from the balanced panel GMM estimator

Abstract

PurposeThe main purpose of this research is to examine the impact of institutional quality and sectoral employment on environmental degradation in developing countries. This paper also re-examined the validity of the Environmental Kuznets Curve (EKC) hypothesis and estimated the long run impact of explanatory variables on CO2 emissions.Design/methodology/approachIn this paper, the balanced panel data for the period 2002–2016 was used based on data availability and applied two-step SYS-GMM estimators.FindingsThe results showed that institutional quality such as government effectiveness (GE) and the rule of law (RL) reduce CO2 emissions and promote environmental quality in developing countries. Interestingly, the authors found new evidence that employment in agriculture and industry has a positive impact on pollution, while employment in the service sector was negatively associated with CO2 emissions, and the validity of the EKC hypothesis was confirmed. In addition, the research suggests that strong institutional frameworks and their effective implementation are the most important panacea and should be treated as a top priority to counteract environmental degradation and achieve the UN Sustainable Development Goals.Originality/valueThis is the first study to examine the short run and long run effects of institutional quality and sectoral employment on environmental degradation using the balanced panel data for a large sample of developing countries. This paper also used a special technique of Driscoll and Kraay standard error approach to confirm the robustness results and showed the different roles of sectoral employment on environmental quality.

Linking governance with environmental quality: a global perspective

“…Our results are partially consistent with the findings of Vogel 89 , Bhattarai and Hammig 90 , Ehrhardt-Martinez et al 91 , Cole and Neumayer 92 , Welsch 93 , Esty and Porter 94 , Fan et al 95 , Culas 96 , Newell 97 , Berkman and Young 98 , Bulkeley 99 , Arvin and Lew 100 , Pour 101 , Newell et al 102 , and Jahanger et al 41 , who employed a single dimension and found that governance (institutional quality) has a negative impact on CO 2 emissions. Nevertheless, our findings fully support the outcome of studies conducted by Shabir et al 38 , Wang et al 39 , Sibanda et al 28 , and Xaisongkham and Liu 40 .…”

“…They also found that the rapid environmental degradation is significantly caused by the reluctance of the government to implement rules and regulations in the region. Xaisongkham and Liu 40 delved into the effects of governance on environmental degradation in a set of selected developing economies from 2002 to 2016. The authors employed the GMM technique and found that the rule of law and government effectiveness are significant factors in reducing environmental degradation in developing countries.…”

Effects of financial development, FDI and good governance on environmental degradation in the Arab nation: Dose technological innovation matters?

“…Moreover, institutions established governmental effectiveness, resource reallocation, and environmental protection in the market-based economy. Additionally, individual and social behavior toward society is purely governed by the established rules, laws and regulations, which is the ultimate contribution of effective and efficient institutions (Mahmood, 2022a;Qamruzzaman, 2022a;Xaisongkham and Liu, 2022). Kirkpatrick and Parker (2004) advocate that good governance has a catalyst in economic transition, indicating the promotion of industrialization with the inclusion of environmental protection through lowering the degree of CO 2 .…”

Corporate restructuring and firm performance in Vietnam: The moderating role of digital transformation

“…We utilize the generalized method of moments (GMM) to investigate how digital transformation moderates the effects of corporate restructuring on firm performance. This statistical technique can deal with various problems in panel estimation, such as autocorrelation, heteroscedasticity, and endogeneity, which could affect the model’s results [ 49 , 50 ]. The GMM method estimates the parameters by minimizing the difference between the observed data and the model’s predicted values based on certain moment conditions that reflect the characteristics of the data-generating process [ 51 ].…”