Global Emissions: A New Contribution From the Shadow Economy

Abstract

Based on the STIRPAT model and the EKC hypothesis, this study provides new evidences on the economic determinants of global emissions. The system-generalized methods of moments estimations are used for the sample of 106 economies in the period of 1995-2012 to investigate the influences of income level, urbanization, industrialization, energy intensity, public expenditure, trade openness, FDI inflow, and especially shadow economy on total greenhouse emissions, CO2 emissions, CH4 emissions, and N2O emissions, respectively. This study contributes to the literature in three folds. First, the industrialization energy intensity are the main drivers for all emissions (excluding N2O). While, urbanization has positive effects on emissions excluding the case of CH4. Other drivers including public spending and economic integration (proxied by trade openness and FDI inflow) are also tested with interesting findings. Second, a higher level of shadow economy increases all emissions excluding CO2. Third, the determinants of emissions vary depending on the countries' income level. The study is supported by a battery of robustness checks and by various estimations in the short and long-run to identify the importance of emissions' drivers.

Impact of shadow economy on renewable and nonrenewable energy consumption in Pakistan: evidence from quantile cointegration and causality analysis

“…The shadow economy might be associated with higher energy consumption, higher energy intensity, and higher non-renewable energy use (Schneider and Enste 2000) leading to a higher level of pollution (Biswas et al 2012). This is, to some extent, consistent with some previous findings showing that shadow activities contribute to an increase of CO 2 via energy consumption (abid, 2015;Canh et al 2019). Notably, our result confirms Sadorsky (2009) who theoretically explained that shadow economy could be an increasing factor for energy consumption due to the possibility for informal activities to avoid environmental regulation policies.…”

Corruption, Hidden Economy and Environmental Pollution: A Spatial Econometric Analysis Based on China’s Provincial Panel Data

“…The Turkish authority needs to focus more on formal and hidden economic activities to prevent environmental degradation in Turkey. Canh et al [26] analyzed the effects of income level, hidden economy, urbanization, industrialization, energy intensity, public expenditure, trade openness and FDI inflow on the emission of carbon dioxide, CH 4 and other pollutants with panel data of 106 economies from 1995 to 2012 and STIRPAT model. They found that industrial energy intensity is the main driver of all emissions.…”

The Shadow Economy-Environmental Quality Nexus In OECD Countries: Empirical Evidence From Panel Quantile Regression

“…Forth, the level of economic development also determines the nature of the informality-environmental quality linkage. In an empirical study of 106 countries during 1995-2012, Canh et al (2019) nd that the relationship between shadow economy and the emission of greenhouse gases, including N 2 O, CH 4 , and CO 2 is conditional on the level of national income. Speci cally, the deregulation effect of the underground economy on environmental quality is dominant in high-income nations, while its scale effect is only witnessed in low and middle-income economies.…”

“…Meanwhile, the control of corruption and expansionary scal policy could alleviate the detrimental impact of underground economic activities on the ecosystem (Biswas et al, 2012;Huynh, 2020). In addition, some studies argue that the level of national income could affect the direction in the informality-environmental pollution linkage, yet their ndings are inconclusive (Canh et al, 2019;Sohail, 2021;Swain et al, 2020).…”