What causes environmental degradation in Pakistan? Embossing the role of fossil fuel energy consumption in the view of ecological footprint.

For decades, environmental degradation has become a universal challenge, and for sustainable environment quality, a true and broader proxy is vital. Pakistan is an ecological deficient country in the world, being the sixth-largest economy (population-wise). This study investigates the prime sources of environmental degradation through ecological footprint in Pakistan. The yearly time-series data spanning 1972 to 2020 is utilized for a set of regressors as fossil fuel energy consumption, trade openness, arable land, industrial share to GDP, economic growth, and population growth. We use various econometric techniques, the bounds test, ARDL (short and long run) model, FMOLS, and Granger causality test. Bounds test confirms the existence of cointegration among variables included in our model. The ARDL estimates suggest that fossil fuel energy consumption, trade openness, and population growth are the leading factors affecting the environment. Fossil fuel consumption and population growth significantly damage the environment in the short and long run. Contrasting to that, trade openness is substantial to the environment quality. The FMOLS approves the robustness of the cointegrating findings. Moreover, a unidirectional causal relationship from economic growth to the ecological footprint (GDP → EFP). And also, the ecological footprint of arable land (EFP → AL) is witnessed. At the same time, bidirectional causality is found between growth rate and fossil energy consumption (GDP ↔ FEC). Lastly, we recommend some policy options to improve environmental quality in Pakistan.

Exploring environment sensitivity to fiscal and monetary policies in China: using ecological footprints as a contemporary proxy.

This paper is a pioneer attempt using ecological footprints, the latest environment sensitivity proxy to be regressed, contributing to the scarce literature concerning one of the most burning global dilemmas of the era. For econometric analysis, fiscal and monetary tools, green energy consumption, and economic growth have been chosen as a set of regressors data spanning 1990-2020 in China facing the highest total ecological footpaths. And giving priority to the relevancy, reliability, and robustness autoregressive distributed lag (ARDL), fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) have been applied for instant and eternal sensitivities, followed by the widely used stationarity tests (augmented Dicky-Fuller and Phillips-Perron tests) and bounds test. Granger's ordeal has also noticed causal inferences. Cointegrating findings are robust across all techniques, and ARDL results remain consistent regardless of short and prolonged duration. We witness positive and statistically significant (at 10%) responsiveness of ecological footprints (EFP) to China's rapid gross domestic output (GDP) growth per capita fueled by fossil fuels (primarily coal). Contrarily, negative/inverse sensitivity to expansionary fiscal (higher government expenditures, GEx), contractionary monetary policies (higher policy rate, DR), and green energy use (REnC). Besides, EFP demonstrates statistically significant reciprocal interconnection with GDP and REnC but a unidirectional connection with DR (DR → EFP). GDP has effective collaboration with REnC and GEx whereas single-sided relationship DR as (GDP → DR). Finally, some policy choices are endorsed.

The relationship between energy consumption, natural resources, and carbon dioxide emission volatility: empirics from G-20 economies.

The present study investigates the causality between energy consumption, natural resources, and carbon emissions volatility. For empirical results, the study analyzed panel data of a Group of Twenty (G-20) countries from 1995 to 2018. The results of Pooled Mean Group (PMG) showed that the consumption of conventional energy sources increases the carbon emissions in the region under consideration. The results also showed that economic growth and carbon emissions are associated with each other according to the Environmental Kuznets Curve hypothesis. The findings showed that rent on mineral resources, oil resources, and forest rent have a positive and significant impact on carbon emissions in G-20 countries. The findings of this study show the complex nature of the relationship between natural resources consumption and carbon dioxide emissions. The study suggests a three-dimensional policy framework for the group of twenty countries of economic cooperation to address the environmental issues with a special focus on natural resources preservation and green economic growth.

The relationship between urbanization, technology innovation, trade openness, and CO2 emissions: evidence from a panel of Asian countries.

This paper explores the dynamic relationship between CO2 emissions, urbanization, trade openness, and technology innovation based on the panel data of 13 Asian countries over the period of 1985-2019. The STIRPAT model is used as a framework for the analysis. For estimation purpose, panel cointegration and FMOLS techniques are utilized. The causality between the concerned variables is also examined by estimating a panel VECM model. The results of panel cointegration reveal the presence of long-run relationship among the variables. FMOLS estimations show that energy consumption increases CO2 emissions while technology change, urbanization, and trade openness compact it. Panel causality analysis indicates bidirectional causality between urbanization and emissions, technology and emissions, trade and emissions, and trade and technology in the long run. Overall findings support the idea that urbanization, technology innovation, and trade openness can play important role to achieve environmental sustainability.

An empirical evaluation of financial development-carbon footprint nexus in One Belt and Road region.

The aspiration of study is to explore the financial development-carbon footprint nexus in One Belt and Road initiative (BRI) region utilizing the panel dataset from 1990-2017. The cross-sectional dependence tests and second-generation panel unit tests were applied to affirm the cross-section dependence and integration level. The panel regression estimators from the Driscoll-Kraay standard error method for robust estimators in the presence of cross-sectional dependence have been applied to compute the estimators concerning the financial development-carbon footprint nexus for One Belt and Road economies. The estimates infer that financial development, urban population, and FDI have an adverse effect on carbon footprint. Furthermore, economic growth and energy consumption pollute the environment by enhancing the carbon footprint. Based on findings, it infers that financial development is a potential instrument to keep the environment through financial reforms. The estimates signify that it is necessary to allocate resources for renewable energy, energy efficiency, and energy conservation projects in order to moderate environmental degradation.