The determinants of life expectancy and environmental degradation in Pakistan: evidence from ARDL bounds test approach.

The current study aims to investigate factors affecting life expectancy in Pakistan with a special focus on environmental degradation measured by carbon emissions (CO2 emissions) on life expectancy from 1975 to 2020. The unit root test results show mixed order integration in the series. The bound F-test and Johansen cointegration test confirm the long-run association between the variables. The long-run estimates of autoregressive distributive lag (ARDL) reveal that CO2 emissions, inflation rate, food production index, and death rate have negative effects on the life expectancy, implying that life expectancy shorten when CO2 increases, while per capita income, urbanization, population growth, birth rate, health expenditure, and education have positive effects on life expectancy, indicating that these factors prolong life expectancy. Moreover, the short-run estimates of ARDL reveal that food production index, urbanization, birth rate, infant mortality rate, and education have positive effects on the life expectancy, while inflation, per capita income, population growth rate, death rate, health expenditure, and CO2 emissions have negative effects on the life expectancy. The findings of the study suggest that the management authorities need to regulate carbon emissions in order to prolong life expectancy which is a key determinant of the economic growth.

Heterogeneity, sunk cost, and export performance: A firm level study of Pakistan's textile sector.

The textile sector is a significant exporting sector of Pakistan. This study examines the determinants of the export performance of Pakistani textile firms using firm-level panel data from 2008 to 2018. Under the framework of the dynamic System Generalized Method of Moment (SGMM) methodology, our findings suggest that both sunk costs and firm-specific characteristics like productivity, age, and size are important determinants of textile exports. Further, the study also observes that firms with a high number of export destinations and greater product diversification tend to export more. The year-specific dummies reveal that textile export performance is adversely affected by the energy crisis in 2012. We recommend that Pakistan should support large, experienced, and productive textile exporter firms to boost textile exports. Besides, more assistance should be provided regarding potential overseas markets to the existing and new export firms.

Oil and natural gas rents and CO2 emissions nexus in MENA: spatial analysis.

Background: Oil rents (OR) and natural gas rents (NGR) have significant contributions to the income of the Middle East and North Africa (MENA) economies and may increase emissions. Moreover, spatial autocorrelation is expected in carbon dioxide (CO2) emissions due to the geographically closed economies in the MENA region. Thus, we examine the impact of OR and NGR on CO2 emissions caring spatial dimensions and analyze the environmental Kuznets curve (EKC). Methods: We apply the spatial Durbin model technique on the effects of OR, NGR, and economic growth on CO2 emissions in 17 MENA nations from 2000-2019, i.e., Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, the United Arab Emirates (UAE), and Yemen. Moreover, diagnostic tests are applied to reach the most appropriate spatial specification and to have the most robust results. Results: The results disclose that CO2 emissions have spillovers and emissions of any country can damage the environment of neighboring countries. The EKC is corroborated with a turning point of 38,698 constant 2015 US dollars. Israel and Qatar are in 2nd phase of the EKC, and 15 MENA economies are in 1st stage. Thus, the economic expansion of most economies has ecological concerns. The effect of natural gas rents is found statistically insignificant. Oil rents have minute negative effects on emissions of local economies with an elasticity coefficient of -0.2117. Nevertheless, these have a positive indirect effect with an elasticity coefficient of 0.5328. Thus, the net effect of oil rents is positive. One percent increase in oil rents could accelerate 0.3211% of emissions. Thus, we suggest the MENA countries reduce reliance on oil rents in their income to avoid the negative environmental effects of the oil sector.

Integration of renewable energy and technological innovation in realizing environmental sustainability: the role of human capital in EKC framework.

For the purpose of this study, the role of technological innovation is examined. Few studies have examined empirically and theoretically the relationship between technological innovation and ecological footprint in conjunction with other factors, such as the human capital index and renewable energy sources, such as biofuels and nuclear power. This study examines the impact of technological innovation on G-7 countries' ecological footprints from 1990 to 2020. A cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model is used in the study. The results of the study show that technological innovation minimizes the ecological footprint. A lower ecological footprint is also associated with increased usage of human capital and renewable energy. Depletion of the natural environment is a short-term and long-term consequence of increased GDP growth. Our results confirm that ecologically sustainable technology enhances the quality of the environment. Consistent panel causality results were achieved. In the context of the G-7 countries, our study's results could support the idea that there are new policy ideas that could help achieve the Sustainable Development Goals (SDG 3, 4, 7, 8, 9, and 13).

FDI, exports, imports, and consumption-based CO2 emissions in the MENA region: spatial analysis.

MENA region is full of natural resources and has a huge mineral sector in their economies. CO2 emissions are increasing global warming and foreign trade and investments can play their roles in determining CO2 emissions in the resource-rich MENA countries. Moreover, spatial linkages are expected in the emissions and trade relationship, which could catch less attention in the environmental literature of the MENA region. Thus, the present research is motivated to capture the contributions of exports, imports, and Foreign Direct Investments (FDI) in consumption-based CO2 (CBC) emissions in twelve MENA economies from 1995 to 2020 by applying Spatial Autoregressive (SAR) Model. Our results exhibit the existence of the Environmental Kuznets Curve (EKC). Moreover, the impact of exports is found negative in direct and total estimates. Thus, exports of the MENA region are reducing CBC emissions in the MENA region and transferring emissions to their importing partners. Moreover, the spillovers of exports are found positive and exports of one MENA country are also responsible for the transfer of CBC emissions to other MENA neighboring countries, which corroborates the trade linkages of the MENA region. Imports have a positive effect on CBC emissions in direct and total effects. This result confirms the fact of energy-intensive imports of the MENA region, which have environmental consequences in the domestic economies and the whole MENA region. FDI increases CBC emissions in direct and total estimates. This result substantiates the pollution Haven hypothesis in the MENA region and is in line with the fact that FDI is mostly coming in the mineral, construction, and chemical sectors. The study suggests that MENA countries should promote exports to reduce CBC emissions and to reduce energy-intensive imports in the region to save the environment from CBC emissions. Moreover, FDI should be attracted to the clean production process and environmental standards should be raised to avoid the environmental problems of FDI in the MENA region.

Heterogeneous role of energy utilization, financial development, and economic development in ecological footprint: How far away are developing economies from developed ones.

This research aims to investigate the impact of energy consumption, financial development, and economic development on the ecological footprint in a panel of 119 developed and developing countries between 2002 and 2018. The study employs panel unit root and autoregressive distributed lag (ARDL) model to achieve this goal. The ARDL results reveal that several factors such as energy consumption, financial development, urbanization, globalization, foreign direct investment, and population growth have a positive relationship with the ecological footprint in developed countries. On the other hand, the human development index and natural resources negatively affect the ecological footprint in developed countries. Moreover, the ARDL results indicate that energy consumption, financial development, urbanization, foreign direct investment, and population growth positively impact the ecological footprint in developing countries in the long run. In contrast, the human development index, natural resources, and globalization have a negative impact on the ecological footprint. These findings imply the need for different policy implications for both developed and developing countries to reduce their ecological footprint.

Harnessing digital solutions for sustainable development: a quantile-based framework for designing an SDG framework for green transition.

The paper investigates how financial technology might help countries promote renewable energy and reach the Sustainable Development Goals (SDGs). It is generally agreed that FinTech (financial technology) has the ability to help achieve the SDGs by 2030 and promote a sustainable society through technology-driven solutions. The financial sector has launched greener investment options in order to mobilize substantial financial resources towards climate neutrality in the coming decade. To achieve the Sustainable Development Goals and the goals set forth in the Paris Climate Agreement, however, this procedure must be accelerated. With the use of the innovative "quantile-on-quantile (QQ)" technique, this study uses the data of top FinTech economies for the period 1990-2020 and provides country-specific insights into the relationship between FinTech and renewable energy. Using quantile causality analysis, we may identify the direction of causality between these variables at the observed extremes. An extensive long-term relationship between FinTech and renewable energy was found in all countries. The leading FinTech economies show a positive association between the two at most quantiles, and a bidirectional causality relationship is seen across significant quantiles. This highlights the considerable yet variable impact FinTech policies have on renewable energy and vice versa in these innovative economies. These results highlight the connection between growing FinTech and promoting a green transition to further Sustainable Development Goals and provide useful insight for policy formulation.

Environmental technology, economic complexity, renewable electricity, environmental taxes and CO2 emissions: Implications for low-carbon future in G-10 bloc.

This study investigates the impact of environmental technological innovation, economic complexity, energy productivity, the use of renewable electricity generation, and environmental taxes on carbon dioxide (CO2) emissions in the G-10 countries for the timeframe from 1995 to 2020. The purpose of the study is to examine the need for a clear plan or strategy to achieve environmental objectives in G-10 countries. In both short-term and long-term projections, the increased use of environment-based technology, economic complexity, and renewable electricity generation has a major positive impact on carbon emission reduction. Moreover, the results demonstrate both unidirectional and bidirectional causality from carbon emissions to renewable energy, electrical generation, and environment-based technologies, respectively. Based on the results, the study proposes a number of concrete policies, such as updating modernized tax systems, increasing tax collection, providing individuals with the means to finance the Sustainable Development Goals through incentive regulations, and making grants from international organizations and the private sector available to finance investments toward the Sustainable Development Goals (SDGs) and carbon neutrality environment targets. This is the study's most significant contribution in order to attain a sustainable and low-carbon future in the G-10 countries, which has policy implications for governments and policymakers.

The impact of geopolitical risk, governance, technological innovations, energy use, and foreign direct investment on CO2 emissions in the BRICS region.

Geopolitical risk (GPR) and other social indicators have raised many somber environmental-related issues among government environmentalists, and policy analysts. To further elucidate whether or not these indicators influence the environmental quality, this study investigates the impact of GPR, corruption, and governance on environmental degradation proxies by carbon emissions (CO2) in BRICS (Brazil, Russia, India, China, and South Africa) countries, namely Brazil, Russia, India, China, and South Africa, using data over the period 1990 to 2018. The cross-sectional autoregressive distributed lag (CS-ARDL), fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) methods are used for empirical analysis. First and second-generation panel unit root tests report a mixed order of integration. The empirical findings show that government effectiveness, regulatory quality, the rule of law, foreign direct investment (FDI), and innovation have a negative effect on CO2 emissions. In contrast, geopolitical risk, corruption, political stability, and energy consumption have a positive effect on CO2 emissions. Based on the empirical outcomes, the present research invites the concentration of central authorities and policymakers of these economies toward redesigning more sophisticated strategies regarding these potential variables to protect the environment.

The dynamic change trends and internal driving factors of green development efficiency: robust evidence from resource-based Yellow River Basin cities.

Promoting the green development of resource-based cities is an essential way to achieve sustainable regional economic development. Based on 2009-2019 panel data of the Yellow River Basin cities, this study adopts the super-directional distance function model to measure the green development efficiency of these selected cities. Furthermore, based on the Malmquist-Luenberger index, this paper focuses on the dynamic change trend of green development efficiency and internal driving factors. Furthermore, the Tobit model is used to specifically explore the influencing factors affecting the green development of cities. The findings suggested that the green development efficiency of selected cities falls in the middle to high range and that the efficiency varies among all cities in the Yellow River Basin. Likewise, technical efficiency improvements and technological progress drive development efficiency, and the former contributes more to green development. However, financial development, energy structure adjustments, and environmental regulation can strongly contribute to the green development of cities, and each influencing factor has obvious temporal and regional differences. This paper proposes appropriate policy suggestions to promote the coordinated development of the economic development and environmental protection of the Yellow River Basin.

Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models.

This article investigates the performance of three models - Autoregressive Integrated Moving Average (ARIMA), Threshold Autoregressive Moving Average (TARMA) and Evidential Neural Network for Regression (ENNReg) - in forecasting the Brent crude oil price, a crucial economic variable with a significant impact on the global economy. With the increasing complexity of the price dynamics due to geopolitical factors such as the Russo-Ukrainian war, we examine the impact of incorporating information on the war on the forecasting accuracy of these models. Our analysis shows that incorporating the impact of the war can significantly improve the forecasting accuracy of the models, and the ENNReg model with the inclusion of the dummy variable outperforms the other models during the war period. Including the war variable has enhanced the forecasting accuracy of the ENNReg model by 0.11%. These results carry significant implications regarding policymakers, investors, and researchers interested in developing accurate forecasting models in the presence of geopolitical events such as the Russo-Ukrainian war. The results can be used by the governments of oil-exporting countries for budget policies.

Asymmetric linkages between renewable energy, technological innovation, and carbon-dioxide emission in developed economies: non-linear ARDL analysis.

Although economic development has been strong in the world's 18 most developed economies, carbon-dioxide emission (COE) has been steadily declining in recent decades. As a result, the purpose of this research is to investigate the role of variables that contribute to the reduction of COE in these economies by using a dataset 1990 to 2019. GDP, [Formula: see text], renewable energy use (REC), and technical innovation (INNO) have been selected as the independent variables for this study. A strategy based on asymmetric ARDL (NARDL) technique is utilized in conjunction with a pooled mean group (PMG) estimation technique to investigate the asymmetrical relationships between COE and the exogenous variables under consideration. For the purpose of determining the direction of causality, the Granger non-causality test is utilized. Furthermore, a unidirectional causality is discovered between GDP and CO2 emissions as well as between GDP and technological innovation. An environmental Kuznets curve hypothesis has been confirmed to exist, and renewable energy has been identified as a significant variable in reducing COE. The study also confirmed that COE is reduced by positive technological innovation shocks and increased by negative shocks. As a result of the findings, the study did a causality test and came up with policy recommendations.

Nexus between the renewable and nonrenewable energy consumption and carbon footprints: evidence from Asian emerging economies.

The study examined the dynamic nexus between carbon footprints, nonrenewable energy and renewable energy consumption, financial development and economic growth, and combating climate change by using a dataset of selected 13 Asian emerging economies (Bangladesh, China, India, Indonesia, Iran, Malaysia, Nepal, Pakistan, Philippines, South Korea, Sri Lanka, Thailand, and Vietnam) from 1995 to 2020. This study empirical analysis uses the second generation of panel cointegration techniques to compensate for cross-sectional dependency and slope heterogeneity. The mean group, the common correlated effects mean group, and the augmented mean group are used to estimate the long-run equations. The findings suggest that economic growth and nonrenewable energy consumption exacerbate environmental degradation, but renewable energy consumption mitigates the total adverse effects on the environment over time. Additionally, economy-specific findings examine how the impact of nonrenewable energy and renewable energy consumption on the carbon footprint depends on energy consumption level. Furthermore, the Dumitrescu-Hurlin causality test reveals a statistically significant bidirectional correlation between financial development, carbon footprints, economic growth, and consumption of nonrenewable energy and renewable energy. Finally, the study says that Asian emerging economies should use more renewable energy and be more efficient in order to reduce their carbon footprints.