A semi-parametric panel data analysis on the urbanisation-carbon emissions nexus for the MENA countries

Carbon emissions and consequent climate change directly affect the sustainable development of ecological environment systems and human society, which is a pertinent issue of concern for all countries globally. The construction of a carbon emission inversion model has significant theoretical importance and practical significance for carbon emission accounting and control. Established carbon emission models usually adopt socio-economic parameters or energy statistics to calculate carbon emissions. However, high-precision estimates of carbon emissions in administrative regions lacking energy statistics are difficult. This problem is especially prominent in small-scale regions. Methods to accurately estimate carbon emissions in small-scale regions are needed. Based on nighttime light remote-sensing data and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, combined with the environmental Kuznets curve, this paper proposes an ISTIRPAT (Improved Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Through the improved STIRPAT model (ISTIRPAT) and panel data regression, provincial carbon emission inventory data were downscaled to the municipal level, and municipal scale carbon emission inventories were obtained. This study took the 17 cities and prefectures of Hubei Province, China, as an example to verify the accuracy of the model. Carbon emissions for 17 cities and prefectures from 2012 to 2018 calculated from the original STIRPAT model and the ISTIRPAT model were compared with real values. The results show that using the ISTIRPAT model to downscale the provincial carbon emission inventory to the municipal level, the inversion accuracy reached 0.9, which was higher than that of the original model. Overall, carbon emissions in Hubei Province showed an upward trend. Regarding the spatial distribution, the main carbon emission area was formed in the central part of Hubei Province as a ring-shaped mountain peak. The lowest carbon emissions in the central area expanded outward, increased, and gradually decreased to the edge of the province. The overall composition of carbon emissions in eastern Hubei was higher than those in western Hubei.

This paper investigates the nexus between carbon emissions (CO2) and economic growth in West Africa based on the Environment Kuznets Curve (EKC) hypothesis by utilizing spatial panel data technique to check the possible effect of spatial dependence among countries in West Africa. Our empirical findings suggest the presence of spatial dependence of carbon emissions distribution in West Africa. By examining the existence of EKC embedded within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) approach, we conclude an inverse N-trajectory of the relationship between carbon emissions and economic growth. Furthermore, to mitigate global carbon emissions, we utilize a recurrent neural network (RNN) bidirectional long short-term memory (BiLSTM) algorithm devoid of exogenous variables and assumptions to forecast carbon emissions from the year 2015 to the year 2030 based on the predictive accuracy of our formulated algorithm. Due to the upward trends in future emission levels, we propose emissions mitigation pathways for countries in West Africa to still hold carbon emissions-related global warming well below 1.5 and 2°C. Such mitigation pathways proposed could help implement strategic policies to minimize carbon emissions to a considerable level. As a policy implication, drafting strict environmental regulations and utilizing renewable energy technologies will help mitigate carbon emissions for all West African countries.

Summary Cities, contributing more than 75% of global carbon emissions, are at the heart of climate change mitigation. Given cities' heterogeneity, they need specific low-carbon roadmaps instead of one-size-fits-all approaches. Here, we present the most detailed and up-to-date accounts of CO2 emissions for 294 cities in China and examine the extent to which their economic growth was decoupled from emissions. Results show that from 2005 to 2015, only 11% of cities exhibited strong decoupling, whereas 65.6% showed weak decoupling, and 23.4% showed no decoupling. We attribute the economic-emission decoupling in cities to several socioeconomic factors (i.e., structure and size of the economy, emission intensity, and population size) and find that the decline in emission intensity via improvement in production and carbon efficiency (e.g., decarbonizing the energy mix via building a renewable energy system) is the most important one. The experience and status quo of carbon emissions and emission-GDP (gross domestic product) decoupling in Chinese cities may have implications for other developing economies to design low-carbon development pathways.

PurposeAgricultural development still constitutes an integral part of Ghana's drive towards job creation, industrial development and economic growth with various growth policies placing the agricultural sector at the core. While there are likely environmental effects of agricultural activities, evidence in Ghana remains scanty. The study focused on examining, empirically, the effects of the development of the agricultural sector on carbon dioxide (CO2) emission in Ghana.Design/methodology/approachThe paper employed the Stochastic impacts by regression on population, affluence and technology (STIRPAT) framework to test for the environmental Kuznets curve (EKC) hypothesis for agriculture and carbon dioxide emission as well as the effect that the changing structure of Ghana's agricultural development has on carbon dioxide emission for the 1971–2018 period. Regression analysis, variance decomposition and causality analysis were performed.FindingsThe regression results revealed a U-shaped relationship between agricultural development and carbon emission, implying a rejection of the EKC hypothesis between the two variables. In addition, the Structural Adjustment Programme was found to positively moderate the effect agriculture has on carbon emission.Practical implicationsThe study recommends the need for policy-makers to facilitate the large-scale adoption and use of modern technology and environmentally friendly agricultural methods.Originality/valueThe study is among the few works to assess the EKC hypothesis between agriculture and carbon dioxide emission in Africa. The direct and indirect effect of structural adjustment programme on carbon emission is estimated.

The contributive effect of industrialization (INDU), urbanization (URB), trade openness (TOR), energy use (ENR), net inflow (FDI), and inclusive growth (INGP) on environmental quality remains a critical global concern, particularly in emerging economies where rapid industrial expansion and urbanization exacerbate environmental degradation. Despite global initiatives to improve the environmental quality of emerging economies, they continue to experience increasing emissions due to fossil fuel dependence, lax environmental policies, and carbon-intensive FDI inflows. Most extant studies focus on developed economies, ignoring a critical gap in understanding how these drivers influence environmental quality in emerging regions. This study addresses this gap using the Environmental Kuznets Curve (EKC), Pollution Haven Hypothesis (PHH), and Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) frameworks. Applying the Panel Autoregressive Distributed Lag (ARDL) model (PARDL) model to panel data (1990–2023) from BRICS, MINT, SAARC, Central Asia, and East and Pacific Asia, we analyze both the short- and long-run effects of these drivers. The results show that INDU and URB significantly degrade environmental quality, increasing CO 2 emissions by 82.4% and 86.0% and GHG emissions by 87.3% and 77.1%, respectively, thus supporting the STIRPAT model, acknowledging INDU and URB as drivers of environmental degradation. TOR and FDI exacerbate environmental degradation due to lax regulations, supporting PHH. This study validates the EKC hypothesis, with emissions declining at higher income levels. While the influences are consistent across blocs, their magnitudes vary. This study advocates green industrialization, stringent regulations, and sustainable energy policies. These results provide insights for policymakers to balance development and environmental sustainability. JEL Classification: O14, Q56, F18, Q53, C33, Q43.

This study selects the panel data of five BRICS nations (Brazil, Russia, India, China, South Africa) from 1990 to 2019 to empirically explore the impact of technological innovation and economic growth on carbon emissions under the context of carbon neutrality. Granger causality test results signify that there exists a one-way causality from technology patent to carbon emission and from economic growth to carbon emission. We also constructed an improved Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The regression results manifest that technology patents contribute to the realization of carbon emission reduction and carbon neutralization, while the economic growth of emerging economies represented by BRICS countries significantly improves carbon emissions, but every single BRICS country shows differentiated carbon emissions conditions with their economic development stages. The impact of the interaction term on carbon emissions for the five BRICS countries also presents country-specific heterogeneity. Moreover, the Environmental Kuznets Curve (EKC) test results show that only Russia and South Africa have an inverted U-shaped curve relationship between economic growth and carbon emissions, whereas Brazil, India and China have a U-shaped curve relationship. There exists no EKC relationship when considering BRICS nations as a whole. Further robustness tests also verify that the conclusions obtained in this paper are consistent and stable. Finally, the paper puts forward relevant policy suggestions based on the research findings.

Fiscal decentralization and green innovation are important to a country’s economic progress, but the externalities of increased pollution as a result of a rise in the energy used and economic growth must not be overlooked. The destruction of the environment presents a serious threat to human existence. South Africa, like several nations, has been working on reducing its dependence on fossil fuels such as coal by utilizing modern energy-efficient technologies that allow to establish a more carbon-neutral economy. Several attempts have been made to identify the major sources of environmental deterioration. Within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework from 1960 to 2020, this study aims to check empirically the effect of fiscal decentralization (FD), green technological innovation (GI), trade openness (OPEN), population size (POP), per capita GDP (GDP), per capita GDP squared (GDP2), institutional quality (INS), and energy consumption (EC) on carbon emissions (CO2) in South Africa, as given its fast economic progress the country is facing problems with CO2 emission. The recently developed novel dynamic autoregressive distributed lag (ARDL)-simulations framework has been used. The outcomes of the analysis indicate that (i) FD, GI, and INS improve environmental sustainability in both the short and long run; (ii) OPEN deteriorates environmental quality in the long run, although it is environmentally friendly in the short run; (iii) per capita GDP increases CO2 emissions, whereas its square contributes to lower it, thus validating the presence of an environmental Kuznets curve (EKC) hypothesis; (iii) POP and EC contribute to environmental deterioration in both the short and long run; and (iv) FD, GI, OPEN, POP, GDP, GDP2, INS, and EC Granger cause CO2 in the medium, long, and short run, suggesting that these variables are important to influence environmental sustainability. In light of our empirical evidence, this paper suggests that the international teamwork necessary to lessen carbon emissions is immensely critical to solve the growing trans-boundary environmental decay and other associated spillover consequences. Moreover, it is important to explain responsibilities at different tiers of government to effectively meet the objectives of low CO2 emissions and energy-saving fiscal expenditure functions.

Fujian Province has entered the golden period of industrialization and rapid economic development, and its economy and society are undergoing significant changes. An unreasonable industrial structure and rapid growth of energy consumption will result in a high pressure of carbon peak and environmental pollution in Fujian Province in 2030. How to improve energy efficiency, control environmental pollution, and achieve a carbon peak by 2030 while ensuring economic growth has become the focus of the attention of researchers and relevant policymakers. A disadvantage of the current 3E (Economy–Energy–Environment) system is that it has no quantitative basis for the selection of variables and no combined analysis of carbon emissions and environmental pollution, which is not conducive to paying attention to environmental pollution in the process of achieving carbon peak. Based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model analysis results of environmental pollution and carbon emissions in Fujian Province, this paper established the 3E system model of Fujian Province to simulate three development scenarios and explored the EKC (Environmental Kuznets Curve). The results of the STIRPAT model showed that population, economic structure, and energy structure were the main influencing factors of environmental pollution and carbon emissions in Fujian Province. The 3E system simulation results showed that the current development scenario (scenario one) in Fujian Province is not sustainable, and the carbon peak and pollutant reduction cannot be achieved in 2030. A more stringent development scenario (scenario three) was required to achieve carbon peak and pollutant reduction on schedule. The trend of the carbon emission EKC curve in Fujian Province was different from that of environmental pollution. The carbon emission EKC curve of Fujian Province was a common inverted “U” shape, while the environmental pollution EKC curve had three shapes of “N”, “M,” and inverted “U”. This study can provide a quantitative method for selecting 3E system variables and a new method for establishing the 3E model, and provide a quantitative reference for Fujian Province to develop subsequent policies to control carbon emissions and environmental pollution.

This chapter examines the relationship between greenhouse gas (GHG) emissions and economic stability in D8 countries – Bangladesh, Egypt, Indonesia, Iran, Malaysia, Nigeria, Pakistan, and Turkey. It aims to assess whether emissions, particularly methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2), serve as economic indicators and how transitioning to renewable energy influences economic growth and environmental sustainability. The chapter employs statistical analysis using regression models to evaluate the impact of GHG emissions on key economic indicators such as gross domestic product (GDP), gross national income (GNI), inflation, and energy consumption. Data from the World Development Indicators (WDI) spanning 2001–2023 are utilized. The Environmental Kuznets Curve (EKC) and Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) models are applied to examine the interplay between emissions and economic growth. Additionally, sectoral analysis is conducted to highlight the role of renewable energy and policy measures in mitigating emissions. The results indicate a complex relationship between emissions and economic stability. Methane and nitrous oxide emissions exhibit a significant correlation with economic performance, whereas CO2 emissions show mixed effects. Countries investing in renewable energy, such as Indonesia and Malaysia, demonstrate improved economic resilience while reducing emissions. The chapter highlights the need for energy efficiency measures, investment in clean energy, and regional cooperation to balance economic growth with environmental sustainability. This chapter provides a comprehensive analysis of the economic impact of GHG emissions in D8 nations, offering policy recommendations to achieve sustainable development while maintaining economic stability.

The need to attain lower carbon dioxide emissions has become a topical issue in recent times. The effect of a number of economic variables on carbon dioxide emissions has been empirically assessed. Rising government expenditure, industrialization, and militarization have characterized many developing countries including Ghana. While it is undeniable that such situation has socio-economic importance to offer developing countries, their environmental effects have become a matter of debate among researchers. This study assesses the carbon dioxide emissions effect of industrialization, government expenditure, and militarization in Ghana. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework, the study models Ghana's carbon emission as a function of income, population, industrialization, government expenditure, and military expenditure. Time series data over the 1971-2018 period was used for investigation. The techniques employed to analyze the data were unit root test, cointegration test, and regression analysis. The autoregressive distributed lag (ARDL) regression approach reveals there is an inverted U-shaped relationship between income and carbon emission confirming the environmental Kuznets curve hypothesis. Also in the long run, carbon emissions are positively influenced by population, industrialization, and militarization but reduced by government expenditure. Similar outcome was obtained in the short run. The paper concludes that the level of income, industrialization, militarization, and population matters to deal with carbon dioxide emissions in Ghana. Policy implications of the findings include the urgent need for authorities to promote the use of eco-friendly production methods for military and industrial activities to sustain the economic growth without harming the environment.

Renewable and Non-Renewable Energy Consumption, Carbon Dioxide Emissions, and Economic Growth: Empirical Evidence from Central Asian Countries Bolor-Erdene Turmunkh Abstract This study examines the relationships between non-renewable and renewable energy consumption, carbon dioxide emissions, economic growth, and population in Central Asian countries after the transition economics with the Panel Granger Causality, Panel Cointegration, and Panel non-stationarity tried to explain using the causality test, using 1992 to 2019 data from the World Development Indicators (WDI). The engagement of developing countries is an increasingly important part of addressing greenhouse gas (GHG) emission-driven climate change. As such, understanding the patterns of energy use, GHG emissions, and economic growth in developing countries is vital. Major Central Asian countries are important in this respect due to their size, rapid growth, and extensive energy reserves. It has experienced rapid growth in its economy, energy consumption, and GHG emissions in recent years. It performs tests to verify the existence of the longrun relationships among the variables and examines short and longrun causal relationships. It finds that increased fossil fuel use is the main cause of increased carbon dioxide (CO2) emissions. Full Text: PDF DOI: 10.15640/jeds.v9n1a7

Effects of land urbanization and land finance on carbon emissions: A panel data analysis for Chinese provinces

Although urban agglomerations have introduced substantial contributions to the economies around the globe, it has also led to the serious environmental challenges. However, this situation may vary across the development levels. The existing knowledge offers a gap in terms of both theoretical and empirical grounds. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) is previously not known to incorporate land agglomeration and the intensity of energy use. Besides, the investigation of linkages among the variables of interest across the development levels within a country is not known to be considered by the existing knowledge. This study systematically investigates the heterogeneous dynamic causality among the intensity of energy use, land agglomeration, carbon dioxide emissions (CO2), and economic progress across the development levels in the Chinese economy, considering 29 provinces for the period 2000 to 2018. To this end, a long-term co-integration association is tested and found existent among the variables of interest. A dynamic common correlated effects mean group approach is applied for impact analysis. The key findings include: The impacts of economic progress and land agglomeration on CO2 are found positive and significant in the country panel and western zone of China (WZC). It turned to be neutral in the case of the central zone of China (CZC) and significantly negative in the eastern zone of China (EZC). To this end, economic progress presented a ‘development ladder-based CO2 mitigation effect,’ while the land agglomeration exposed the ‘land agglomeration ladder-based CO2 mitigation effect’. Further, the causalities extracted are: first, economic progress is found in positive bilateral linkages with the intensity of energy use and land agglomeration for all the panels. Second, a positive and unilateral causal bridge is found operating from land agglomeration to the intensity of energy use and from the intensity of energy use to CO2. Third, a unilateral linkage of mixed nature is exposed to exist from land agglomeration to CO2, with positive causal links for country panel and WZC, negative causal links for EZC, while a neutral linkage is found for CZC. Fourth, a bidirectional link with mixed causalities appeared in the country panel and WZC. Economic progress increased CO2 in WZC. Next, a negative bilateral link is observed between the two variables in EZC. Additionally, this link remained neutral in CZC. Based on empirics, it is revealed that the development level matters in determining the links among the variables of interest.

The environmental Kuznets curve (EKC) hypothesis has become an important factor in environmental studies in recent years. D8 members have viable economic positions in their respective regions due to their natural resources, crowded populations and potential markets sizes. This study deals with the validity of the EKC hypothesis for D8 countries between the years 1972 and 2014. The main contribution of this study to the literature is to identify the relationship between carbon emissions, GDP and energy use variables in D8 countries. Thanks to the model used, the relationship in the inverse N and N form was estimated and turning points were calculated. Furthermore, this relationship supports the N-shape environmental Kuznets curve hypothesis. In the light of these results, policymakers should immediately put policies in place that aim at reducing carbon emissions. The panel results of our study show that there is an inverse N-shaped relationship. The country with the highest per capita energy use and the highest carbon emission is Turkey, followed by Indonesia. In Malaysia, however , increase in per capita GDP causes a decrease in the carbon emission of per capita energy use. Therefore, Turkey and Indonesia’s clean energy use needs to take steps towards encouraging production which implements the policy.

India is excelling in almost all parameters of economic development. However, India is experiencing economic development with a pinch of salt; massive climate and environmental changes are seen. As per Global Climate Risk Index 2021, India was ranked as the 7th worst climate hit country in the world. The present study is an attempt to empirically evaluate the impact of India’s economic growth, population, foreign investment, and trade openness on carbon emissions. The study adopts the STochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework augmented with the Environment Kuznet’s Curve (EKC) model with a time series data over the period 1971–2020. Results for unit root testing indicated the presence of mix stationarity for the variables, therefore, the autoregressive distributed lag approach was apt for capturing the long- and short-run dynamics. The results validated EKC for affluence and population. The outcome for FDI inflows and trade openness suggested the presence of pollution haven hypothesis for India. Policy initiatives toward green and sustainable production processes are suggested.