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Abstract This paper investigates the impact of urbanization on energy consumption by applying the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) in case of Malaysia. The study covers the time period of 1970Q1–2011Q4. The unit root test and the ARDL bounds testing approach have been applied to examine integrating properties and long run relationship in the presence of structural breaks. Our results validated the existence of cointegration and exposed that urbanization is a major contributor in energy consumption. Affluence raises energy demand. Capital stock boosts energy consumption. Trade openness leads to affluence and hence increases energy consumption. The causality analysis finds that urbanization Granger causes energy consumption. The feedback effect is found between energy consumption and affluence and, energy consumption and capital. The bidirectional causality exists between trade openness and energy consumption.

Abstract Energy poverty has evolved as an important issue of research from academic and policy perspectives during the last two decades. The energy poverty estimates exhibit widespread variations across regions, methodologies and datasets, making the task of energy and development policies challenging. The variations in energy poverty estimates necessitate a systematic and rigorous analysis of these empirical studies to obtain a true measure of energy poverty. The present study conducts a meta-analysis of 30 studies and 103 estimates of energy poverty. On average, 71.1 percent of the population in the overall sample studies has been identified as energy poor. High I-square statistic values suggest heterogeneity bias in the sample studies; therefore, analysis at the subgroup level has been conducted. Meta-regression analysis of overall sample and subgroups has identified type of methodology, citations, impact factor of the journal, regional dummies, average key crude oil price and gross domestic product as significant factors that explain heterogeneity in the energy poverty effect sizes. We recommend inclusion of local and regional factors in assessment of energy poverty for developing a uniform methodology to assess energy poverty across developing economies.

A key factor in social development and sustainable urban expansion is the establishment of sustainable and affordable transport systems. This study aims to investigate the impact of transport infrastructure investment on environmental degradation and economic growth and to test the validity of the EKC hypothesis in China, India, Russia, and Japan over the period 1995-2020. The results show that GDP has a significant positive effect, and GDP2 and GDP3 have significant adverse effects on environmental degradation, respectively. These results confirm the validity of the inverted U-shaped EKC hypothesis in selected emerging Asian economies. Rail infrastructure investment has significant adverse effects, while road infrastructure investment and aviation infrastructure investment have significant positive effects on environmental degradation. Likewise, the impact of investment in transport infrastructure system (roads, rail, and aviation) on economic growth is positive and statistically significant. Country-level estimates confirm the validity of the inverted U-shaped EKC hypothesis in China, India, and Russia, while the N-shaped EKC is only valid in Japan. Investments in rail infrastructure based on modern rail systems that run on electricity are believed to be less polluting in the transport mix, help create sustainable and safe transport systems, and reduce emissions at the urban and intercity levels in emerging Asian countries. In addition, the growing impact of free trade on environmental pollution should be strengthened to harmonize the strict enforcement of environmental conditions dominated by trade agreements.

Environmental quality indicators are crucial for responsive and cost-effective policies. The objective of the study is to examine the relationship between environmental quality indicators and financial development in Malaysia. For this purpose, the number of environmental quality indicators has been used, i.e., air pollution measured by carbon dioxide emissions, population density per square kilometer of land area, agricultural production measured by cereal production and livestock production, and energy resources considered by energy use and fossil fuel energy consumption, which placed an impact on the financial development of the country. The study used four main financial indicators, i.e., broad money supply (M2), domestic credit provided by the financial sector (DCFS), domestic credit to the private sector (DCPC), and inflation (CPI), which each financial indicator separately estimated with the environmental quality indicators, over a period of 1975-2013. The study used the generalized method of moments (GMM) technique to minimize the simultaneity from the model. The results show that carbon dioxide emissions exert the positive correlation with the M2, DCFC, and DCPC, while there is a negative correlation with the CPI. However, these results have been evaporated from the GMM estimates, where carbon emissions have no significant relationship with any of the four financial indicators in Malaysia. The GMM results show that population density has a negative relationship with the all four financial indicators; however, in case of M2, this relationship is insignificant to explain their result. Cereal production has a positive relationship with the DCPC, while there is a negative relationship with the CPI. Livestock production exerts the positive relationship with the all four financial indicators; however, this relationship with the CPI has a more elastic relationship, while the remaining relationship is less elastic with the three financial indicators in a country. Energy resources comprise energy use and fossil fuel energy consumption, both have distinct results with the financial indicators, as energy demand have a positive and significant relationship with the DCFC, DCPC, and CPI, while fossil fuel energy consumption have a negative relationship with these three financial indicators. The results of the study are of value to both environmentalists and policy makers.

Globalization has resulted in several technical advancements, including the ability to connect people all over the world and drive the economies with higher agricultural output. With agricultural productivity expanding quickly, the negative impact of globalization on environmental degradation is being disregarded. Rapid agricultural expansion and globalization have resulted in significant increases in energy consumption and CO2 emissions. The primary purpose of this research is to assess the role of Pakistan’s massive agriculture industry in encouraging or discouraging CO2 emissions under Globalization scenario. Therefore, we applied Non-linear Autoregressive Distributive Lag Nonlinear Autoregressive Distributed Lag model from 1971 to 2021. Our results showed that in presence of globalization, agricultural production shows asymmetries in case of positive and negative shocks. A positive shock in Agricultural production increased the CO2 emissions while negative shock in agricultural production decreased CO2 emissions. Furthermore, GDP, energy consumption and economic globalization have positive association with economic globalization while on the other hand, surprisingly trade and urbanization in the presence of globalization have negative association with CO2 emissions. Environmental deterioration due to greenhouse emissions causes climatic variation in the economy and several mitigation strategies are required on sustainable basis in Pakistan. So, our study recommends that farmers of Pakistan should adopt organic farming this will help to reduce CO2 emissions.

Using time-series data from 1984 to 2019, the study examines the vigorous trade-environment relation in Pakistan. Pakistan is an interesting case study in which trade liberalization has expanded economic activity while also increasing environmental pollution during the last two decades. As a result, determining whether trade and industrial operations have contributed to environmental degradation is crucial. Our first goal is to look at how trade affects the environment in terms of scale, composition, and technique. The second step is to look into the pollution haven theory. The study uses a new approach to measuring trade openness called composite trade intensity, which differs from the traditional approach. The dynamic autoregressive distributed lag (ARDL) simulation framework, which was recently developed, was employed. The findings show that the scale impact raises CO2 emissions while the technique effect helps to lessen them, proving the existence of an environmental Kuznets curve (EKC) hypothesis. The composition impact contributes to increased pollution in the environment. Through the expansion of pollution-intensive export businesses, trade openness degrades environmental quality over the long as well as in the short term. The notion of a pollution hypothesis has also been proven. The quality of the environment deteriorates as a result of urbanization, whereas it improves as a result of good governance. Economic growth, trade openness, urbanization, and CO2 emissions have bidirectional causality, according to frequency domain causality findings. Based on our empirical findings, the study concludes that individual efforts, as well as collective efforts at the international level to reduce carbon emissions, are critical to solving the problem of environmental degradation and making the world a completely peaceful place.

A sustainable financial and economic system is the need of the hour, especially in the context of a deteriorating environment. Environment quality is very important for healthy labour life, contributing to economic growth more effectively. China is one of the largest economies in the world, which is the residency of 18.47% of total humans on planet earth. Keeping environment is a major policy concern; this study investigates the relationship between environmental quality and economic growth in the presence of human capita and renewable energy use. The study uses the data for the time period of 1995–2017. While conducting nonparametric tests, the study applied QMMR regression approach to hand the issues of cross-sectional heterogeneities and endogeneity simultaneously. The study found that GDP is positively associated with carbon emissions throughout all the quantiles. Foreign trade is showing mixed results, most probably due to changes in trade patterns, and a hidden heterogeneity across provinces in China.

Abstract Energy indices provide a clear summary to measure the energy performance of the country which is a prerequisite for policy making. Increasing rate of greenhouse gases (GHGs) emissions are causing dangerous climate changes which seem irreversible. Therefore, it is vital to rank countries with the highest GHGs and carbon emissions in order to develop low carbon economies. This requires a comprehensive statistical analysis. In this study, we develop an aggregated composite index (ACI) of energy security and environmental sustainability for each of the world’s highest GHGs and CO2 emitting countries. Our index is based on a comprehensive set of indicators including carbon emission and energy metrics. Composite indicator has been used to combine all the indicators in a holistic way. Higher values show a better efficiency and vice versa. The analysis reveals that there are considerable differences in performances of top and low ranked countries, Canada and Brazil respectively. We ranked the countries based on their efficiency score from top-down. Analysis also provides a roadmap and guidelines for the future policymakers.