Stochastic Impacts By Regression On Population, Affluence, And Technology Research Articles

Inversed virtual water flow pattern and its influencing factors in Northwest China

Northwest China faces an imbalance between scarce water resources and increasing water use demands for agricultural production and economic growth. However, the shifts in virtual water flow (VWF) patterns in Northwest China and its the driving factors are still unclear. In this study, we employed the Multiregional Input–Output (MRIO) method to investigate the evolution of VWF patterns among Northwest China’s primary, secondary, and tertiary industries and analyze their driving factors through Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The results show that (1) Northwest China changed from 7.24 × 108 m3 net inflow to 175.70 × 108 m3 net outflow from 2002 to 2007. (2) More than 90 % of virtual water of the primary industry was used locally, but both secondary and tertiary industries depended more on virtual water inflow in Northwest China. (3) The virtual water outflow from Northwest China is mostly to the Jing-Jin Region, followed by the Northern Coast, and the main inflow is from the Central Region. (4) Population, economy, and technology affect virtual water from inflow to outflow, and technological development helps to decrease virtual water flow volumes. In conclusion, dramatic decrease of second and third industries inflow is the main reason for the changes from net inflow to net outflow of Northwest China. Meanwhile, it is feasible to increase technological support to further reducing virtual water consumption in primary industry, and strengthen virtual water trades of secondary and tertiary industrial virtual water inflows by promoting population and economic conditions.

Impacts of ICT penetration shaping nonworking time use on indirect carbon emissions: Evidence from Chinese households

People's time use is crucial for carbon emissions related to lifestyles. Increasing the adoption of information and communication technology (ICT) in everyday life changes time-use patterns and, thus, affects emissions. This study proposes a two-way fixed effects model following the stochastic impacts by regression on population, affluence, and technology (STIRPAT) framework to explore the impacts of ICT penetration and household average weekly nonworking time on household indirect emissions. Based on 2010–2018 panel data from the Chinese Family Panel Studies (CFPS), the results show that a 1% increase in household average weekly nonworking time can lead to a 0.169% decrease in emissions by substituting consumption with time. In contrast, a 1% increase in the proportion of family members using a mobile phone or the proportion of family members surfing the internet can increase emissions by 0.107% or 0.127% by increasing electricity and communication consumption. ICT penetration moderates the relationship between nonworking time and emissions, which suggests that ICT amplifies the impact of nonworking time by reducing the consumption of activities and shaping the time-use pattern to be less carbon intensive.

Assessing the impact of changing environment on coal mining water use in China

The unequal distribution of coal and water resources, coupled with efforts to enhance coal production efficiency, has exacerbated the issue of water scarcity in China’s coal mining industry. There is an urgent need to analyze the impact mechanism of external environmental changes on coal mining water use. With partial least square regression and vector autoregressive model under the framework of STIRPAT (stochastic impacts by regression on population, affluence, and technology) model, this paper systematically investigates the impact of population, economic level, energy consumption structure, and water intensity on coal mining water use, as well as the long-term dynamic response of coal mining water use to various variables. Results show a significant positive correlation between the economic level and coal mining water use, and the economic level has the greatest impact on changes in coal mining water use. Water intensity is negatively correlated with water used for coal mining. Decreasing water intensity in coal mining has a rebound effect. Water intensity shows a negative effect in the short term, but gradually disappears in the long run. Energy consumption structure positively correlated with coal mining, but its correlation and influence degree are weak. Promote green and low-carbon energy transformation, water-saving technologies application, and unconventional water utilization, and further improve the sustainable development guarantee capacity of the coal industry.

What impacts do green bonds have on carbon emissions and how? A dynamic spatial perspective in China.

Green bonds are adopted to exclusively finance or refinance green projects and thus is an important policy instrument for sustainable development. The development of these eco-friendly projects benefits the reduction of carbon emission. What impacts do green bonds have on carbon emission intensity and how? This issue needs to be revealed, including the dynamic spatial interactive rules and regional heterogeneity. This is especially true for China, with its vast territory and a short history of green bonds. Different with existing literature, this paper collects the data of the amount of green bond issued in each province in China rather than the policy dummy variable of green bond. The spatio-temporal interactions of both the impact of green bonds and the related mechanisms are studied. A dynamic spatial Durbin model (DSDM) combined with a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model reveals the negative impacts of green bonds on carbon emissions and the spatio-temporal interactions. On this basis, the mediation model is introduced to clarify the three impact mechanisms of green bonds and find the predominant role of technology mechanism. In addition, different characteristics in spatial interactive rules and impact mechanisms of green bonds are found in various regions of China. Finally, the study proposes some policy recommendations on how to effectively reduce carbon emissions with green bonds.

Socioeconomic driving forces behind air polluting emissions in Mexico.

Air pollution is one of the most severe environmental problems that Mexico is currently facing. The objective of this paper is to quantify the most relevant socioeconomic driving forces behind air polluting emissions and, more specifically, 7 local pollutants in Mexico. We do so in a multilevel version of the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model that accounts for the spatial heterogeneity at the municipal level across the country. The results show that the most relevant variables to determine the emissions of atmospheric pollutants are the population, the harvested area and the number of cars, while technological development helps to mitigate such emissions. The ecological elasticities are, in all cases, smaller than one. Our purpose is to provide quantitative information about these socioeconomic driving forces of air deterioration as a basis to establish some recommendations for environmental policy decision-making.

Study on the Emission Reduction Effect of Green Technology Innovation

Abstract Green technology innovation is one of the important ways to achieve carbon emission reduction targets. Based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and the fixed-effects model, we measured CO2 emissions by region using regional panel data from 2005 to 2021 at provincial level in China and explored the CO2 emission reduction effect. Based on this, we analyzed heterogeneity of green technology innovation and the mechanism of the effect. The results show that green technology innovation has a significant effect on carbon emission reduction, achieving a 5% reduction. Second the CO2 emission reduction effect of green technology innovation varies according to differences in economic, scientific and technological human resources, and environmental management inputs. It shows that green technology innovation has a significant CO2 emission reduction effect in economically developed regions, regions with sufficient scientific and technological human resources, and regions with high environmental management inputs. Third, green technology innovation mainly achieves CO2 emission reduction by improving the level of cleanliness. JEL classification numbers: P28, O14, O15, O18. Keywords: Green technology, Carbon emissions, STIRPAT model, Means to reduce emissions.

Does Economic Policy Uncertainty Cause Environmental Pollution? Fresh Evidence From Developed Countries.

The industrial revolution has dramatically altered the environment and ecosystem. So many scholars have empirically attempted to reveal the most influential anthropogenic factors on environmental degradation. For this purpose, this study examines the leading determinants of CO2 emissions in the context of economic policy uncertainty (EPU) for 14 developed countries within the framework of the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) environmental model from 1997-2018. For empirical modeling, CO2 emission is treated as the dependent variable, which is a strong proxy for environmental degradation. In addition to the GDP per capita, population density, and energy intensity (a proxy for technology), the basic model is extended to include variables such as EPU, renewable energy, trade openness, globalization, and information and communications technology (ICT) index. While the estimation results by the dynamic conditional correlation (DCC) estimator, which are also supported by robustness analysis, suggest that GDP per capita and energy intensity are the main contributors to emission levels, population density has no significant impact on CO2. Furthermore, while renewable energy (in model 2), trade openness (in model 4), and globalization (in model 6) have negative impacts on CO2 emission, technology (in models 5 and 6) and EPU (in model 6) make marginal contributions to CO2.

Optimization of Urban Road Green Belts under the Background of Carbon Peak Policy

To explore the reasonable width of urban road green belts under the background of carbon peak policy and provide quantitative design guidance for urban green-belt planning, a closed-loop design that integrates urban traffic, carbon emissions, urban greening, and carbon absorption was established at the road network level. First, the factors affecting urban transport carbon emissions were analyzed from the aspects of environment, economy, population, and technology. A carbon emission prediction model was constructed based on the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The path of the carbon peak in the transportation sector was simulated. A scenario under the carbon peak target was designed to determine the development trend of each factor. The layout forms and design requirements of urban road greening were then summarized. The annual carbon sequestration amounts of different plant types were calculated. An objective optimization model was constructed with the lowest cost of greening as the objective function. The constraint conditions specify that the carbon absorption be greater than the carbon emissions, in addition to specifying the basic greening design requirements. Finally, an empirical study was conducted on a road network area in Xi’an. According to the results, the traffic carbon emissions of Xi’an City will be 2.71 Mt in 2025, with road traffic accounting for 1.94 Mt. Without considering the road-measurement green-width constraint, the proportions of the road green belt for six road classes and the red-line width under the carbon peak target are 0.31, 0.33, 0.40, 0.22, 0.21, and 0.23. These research results provide a quantitative and reliable basis for designing the width of an urban green belt under the background of carbon peak policy. Under proportion restrictions, road greening yields better performance by considering both aesthetics and road characteristics.

The Impact of Fossil Fuels, Renewable Energy, and Nuclear Energy on South Korea’s Environment Based on the STIRPAT Model: ARDL, FMOLS, and CCR Approaches

This study intends to shed light on the environmental impacts of energy decisions in South Korea by analyzing the correlation between energy consumption patterns and environmental indicators such as carbon dioxide emissions. In 2021, global CO2 emissions increased by 6%—to the highest ever level of 36.3 billion tons—according to the International Energy Agency (IEA). This increase in CO2 emissions is a big problem for all countries around the world. The aim of this article is an analyze the impact of fossil fuels, renewable energy, and nuclear energy on South Korea’s environment based on the STIRPAT (stochastic impact by regression on population, affluence, and technology) model. Exploring the intricate nexus between economic and energy indicators and environmental outcomes, this study employs the STIRPAT model to analyze the influence of GDP, population dynamics, fossil fuels, renewable energy, and nuclear energy on South Korea’s environment. The yearly data from 1972 to 2021 are analyzed in this paper using an autoregressive distributed lag (ARDL) model. The reliability of this study is also examined by employing FMOLS (fully modified ordinary least squares) and CCR (canonical cointegrating regression) estimators. This study confirms the findings of previous research by showing that the rising South Korea GDP and population can lead to higher CO2 emissions and that a strategy switching to renewable energy can cut down on CO2 emissions in Korea, as it exhibits a coefficient of −0.085 *. The robustness results of FMOLS and CCR’s findings support baseline ARDL findings.

The Interplay of Green Technology and Energy Consumption: A Study of China’s Carbon Neutrality and Sustainable Digital Economy

In light of the carbon neutrality goals set post-Paris Climate Conference (COP21), this study delves into the relationship between green technology innovations, energy consumption, and CO2 emissions in China, spanning the period of 1990 to 2021. The objective of this paper is to creatively present the idea of a low-carbon digital economy from the viewpoint of digital technology. Utilizing the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we scrutinize this relationship, employing unit-root testing to verify the integrative attributes of the variables, inclusive of structural break data. Further analysis using the bootstrap Autoregressive Distributed Lag (ARDL) bound testing method corroborates the relationship between these key variables. The study reveals unidirectional co-integration over time among green technology innovations, renewable and non-renewable energy, per capita income, population, and CO2 emissions as per the Granger causality test. Interestingly, our findings suggest that while green technology innovation, per capita income, and renewable energy contribute to the reduction of CO2 emissions, non-renewable energy consumption and population growth exacerbate them. These insights offer valuable guidance for policymakers in formulating comprehensive strategies to enhance environmental quality through the promotion of renewable energy and green technology innovations, with a specific emphasis on the Chinese context.

How does monetary policy moderate the influence of economic complexity and technological innovation on environmental sustainability? The role of green central banking

AbstractThe debate on whether central banks should consider climate change risks in their mandate could reach some informed consensus given it is explicit how monetary policy, directly and indirectly, interacts with environmental sustainability. To unriddle this issue, we touch upon a novel perspective by exploring how monetary policy moderates the influence of economic complexity and technological innovation on energy productivity and carbon productivity in the presence of the labour force participation ratio, trade openness, financial inclusion, and GDP. Our study builds upon the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) structure, considering the Group of Seven (G7) countries as the analytical laboratory over the 1995–2019 period. The principal outcomes based on the novel Method of Moments Quantile Regression (MMQR) are as follows: A higher level of economic complexity is associated with enhanced environmental sustainability. In addition, technological innovation is beneficial to environmental sustainability, especially across countries reporting moderate levels of environmental sustainability. Considering the direct impact, contractionary monetary policy deteriorates environmental sustainability, while expansionary monetary policy promotes it for green central banking in place. Regarding the moderating impacts, expansionary monetary policy is unveiled to manifest beneficial environmental impacts of economic complexity and technological innovation, provided that inflationary pressures remain under control. Based on our findings, it is recommended to formulate a monetary policy inclusive of climate‐related risks to capitalize on the environmentally favourable impacts of economic complexity and technological innovation. Finally, the monetary expansions keeping the inflationary pressures under control would benefit environmental sustainability.

The trade-environment nexus in global cereal trade: Combing social network and spatial panel econometrics analysis

The global trade networks of maize, wheat, and rice and their carbon emission networks are established from 2000 to 2020. Social network analysis (SNA) is used to quantify countries' roles in the networks. Spatial econometric model is used to analyze the impacts of socio-economic indicators and the countries' roles according to Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. The results show that the global cereal trade volume and their carbon emissions continues to rise. The overall cereal trade networks tend to be complicated and tight. Global cereal trade networks are highly heterogeneous networks with numerous peripheral economies and a few hub countries. However, with the rise of developing economies in cereal trade, the dominance of hubs decrease. The panel regression shows that the network characteristics of three cereal trade have different effects on carbon emissions as follows: (1) In rice trade network closeness centrality increases carbon emissions, meaning countries or regions with more trade connectivity emit more carbon emission. However, in wheat trade network it has reducing effects in whole and low-income countries, while increasing effects in high-income countries. (2) Among the three cereal trade, betweenness centrality reflecting mediating role has significantly negative effects on carbon emissions only in rice trade network. (3) Regarding the number of importing partners, it has positive effects on carbon emissions for wheat and maize trade, while negative for rice trade. The number of exporting partners has positive effects in all the three cereal trade. This study confirms carbon mitigation potentials from trade relationship. This study also discusses some implications for agriculture and policymakers in different countries or regions involved in the global cereal trade.

Impact of Population, Trade Openness, and Foreign Investment on India’s Environment: An Empirical Evaluation of a STIRPAT Model

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.

Unveiling the liaison between financial development dimensions, energy efficiency and ecological footprint in the context of institutional frameworks: evidence from the Emerging-7 economies.

Financial development and energy efficiency can facilitate the transition towards a more environmentally sustainable and responsible economy. Simultaneously, the importance of institutional effectiveness cannot undermine the need to manage financial and energy consumption activities. To this end, the primary objective of this study is to examine the effects of financial development and energy efficiency on the ecological footprint of the Emerging-7 economies from 2000 to 2019. The study specifically focuses on the influence of these factors within the context of robust institutional mechanisms. We employ the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model as the analytical framework to accomplish this. This study takes into consideration three aspects of financial development, which are: (i) the depth of financial development, (ii) the stability of financial development, and (iii) the efficiency of financial development. In addition, this study has developed an institutional index using principal component analysis. The index comprises several crucial indicators: Control of Corruption, Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law, and Voice and Accountability. The study raises the importance of energy efficiency in terms of energy intensity on ecological footprint. The study's findings suggest that without robust institutional mechanisms, the potential of financial development depth, stability, and efficiency to improve ecological well-being may not be fully realized. However, the study concludes that these institutional mechanisms positively impact mitigating the ecological footprint.

A Review of Building Carbon Emission Accounting and Prediction Models

As an industry that consumes a quarter of social energy and emits a third of greenhouse gases, the construction industry has an important responsibility to achieve carbon peaking and carbon neutrality. Based on Web of Science, Science-Direct, and CNKI, the accounting and prediction models of carbon emissions from buildings are reviewed. The carbon emission factor method, mass balance method, and actual measurement method are analyzed. The top-down and bottom-up carbon emission accounting models and their subdivision models are introduced and analyzed. Individual building carbon emission assessments generally adopt a bottom-up physical model, while urban carbon emission assessments generally adopt a top-down economic input-output model. Most of the current studies on building carbon emission prediction models follow the path of “exploring influencing factors then putting forward prediction models based on influencing factors”. The studies on driving factors of carbon emission mainly use the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the Logarithmic Mean Divisia Index (LMDI) model, the grey correlation degree model, and other models. The prediction model is realized by the regression model, the system dynamics model, and other mathematical models, as well as the Artificial Neural Network (ANN) model, the Support Vector Machine (SVM) model, and other machine learning models. At present, the research on carbon emission models of individual buildings mainly focuses on the prediction of operational energy consumption, and the research models for the other stages should become a focus in future research.

Analysis of spatiotemporal evolution characteristics and peak forecast of provincial carbon emissions under the dual carbon goal: Considering nine provinces in the Yellow River basin of China as an example

At present, China is facing substantial pressure to reduce carbon emissions (CE). In the context of China's dual carbon goals, the accounting and peak prediction of CE in provinces along the Yellow River Basin (YRB) are crucial to China's carbon reduction strategy. In this study, the IPCC inventory method was used to calculate the CE of the provinces in the YRB, and the spatial and temporal evolution characteristics of CE are described. The Tapio decoupling model was used to explore the decoupling relationship between CE and economic growth. By establishing the stochastic impacts by regression on population, affluence and technology (STIRPAT)-ridge model, based on the impacts of CE drivers, the carbon peak of each province is predicted, and a differentiated carbon peak path is proposed. The results indicated that from 2005 to 2020, the total CE amount in the YRB continued to rise, but the growth rate continued to decline. Provinces experienced many changes involving strong decoupling, and the decoupling state varied. Factors such as the permanent resident population, economic level, technological level, urbanization level, industrial structure, and energy intensity drove CE changes, of which population was the main CE driving force. CE in the YRB will peak between 2030 and 2044, and the cumulative emissions will be in the range of 6913.96–16248.6 Mt CO2eq, while 2030 is suggested as the best year to peak. The research results provide a scientific basis for different regions to formulate differentiated carbon emission reduction plans.

A revisit of the EKC hypothesis in top polluted African countries via combining the ARMEY curve into the Kuznets curve: A Fourier ARDL approach.

The present research reexamines the validity of the EKC hypothesis in the top three polluted nations in Africa, Algeria, Egypt, and South Africa, over the period 1970-2020. The central idea of the research is to reexamine the EKC hypothesis by incorporating the ARMEY curve linking government spending and GDP into the Kuznets curve, as suggested by Işık et al. Environ Sci Pollut Res 29(11):16472-16483, (2022) and Ongan et al. Environ Sci Pollut Res 29(31):46587-46599, (2022). To this end, the ARDL equation with a Fourier function is implemented to estimate the long-run drivers of environmental deterioration. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model results revealed that the composite model is only valid in Algeria, and the optimal government spending that maximizes CO2 emissions is 16.88% of gross domestic product. On the contrary, the results showed that the composite model is not valid for South Africa and Egypt due to the failure of the desired shapes for the three curves. The outcomes also confirm the role of energy consumption and population as key drivers of environmental degradation in the three countries.

How are green finance, carbon emissions, and energy resources related in Asian sub-regions?

In this research, by employing the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, for the case studies of five Asian regions and data from 2010 to 2021, the impacts of green finance and renewable resource deployment on CO2 emissions are evaluated. The results confirmed that the effect of green financial development and expansion of renewable energies on carbon dioxide emissions does not have the same pattern in Asia. In Central and South Asia, the development of green finance, patents, and the expansion of renewable energies do not significantly affect carbon dioxide emissions. In contrast, in East Asia, green financial development, exploitation of renewable energies, and patents will reduce carbon dioxide emissions in the short and long term. Accelerating the operationalization of ongoing green projects, promoting FinTech green financing tools, increasing green facilities, implementing tax exemptions, and supporting green research and development are suggested as effective practical policies.

How does green credit reduce carbon emissions? Dynamic spatial interactions and regional disparities

Green credit is an important green financial policy tool to promote green development. However, research is needed to explore how green credit reduces carbon emissions, especially with respect to its dynamic spatial interactions and regional disparities. Based on a theoretical analysis, this paper empirically tests the carbon emission reduction effect of green credit and its three mechanisms by combining a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, dynamic spatial Durbin model (SDM), and the mediation model, including their dynamic spatial interactions and regional disparities. The study concludes that green credit can reduce carbon emission intensity based on strong spatio-temporal interactions in China. Green credit mainly reduces carbon emission intensity through scale and technology mechanisms with different spatio-temporal interactions. The tertiary industry in China does not currently have completely clean production; as such, the upgrading of the industrial structure as stimulated by green credit in the long term cannot yet effectively reduce carbon emissions. In addition, the carbon emission reduction effect of green credit and its three mechanisms have different levels of performance and dynamic spatial interactions in different regions of China. Finally, targeted policy recommendations are proposed to apply green credit to effectively reduce the carbon emission intensity.

Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China

It is crucial for China to take the characteristics and development stage of every province in the region into account in order to achieve the “dual carbon” development goal. Using data collected from 2000 to 2019, this study identifies the factors that influence carbon emissions using the logarithmic mean Divisia index (LMDI) method and establishes a revised stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to investigate the effects of four key factors on carbon emissions in Jiangsu province: population size, economic output, energy intensity, and energy structure. The following conclusions were drawn: (1) energy intensity contributes to a slowed rate of carbon emission production in Jiangsu, whereas population size, economic output, and energy structure contribute to a pulling effect; (2) under different scenarios, Jiangsu’s carbon dioxide emissions peak at different times and reach different values; and (3) two low-carbon scenarios are more in line with the current development situation and future policy orientation of Jiangsu Province and are therefore better choices. Our policy recommendations are as follows: (1) the development of economic and social activities should be coordinated and greenhouse gas emissions should be reduced; (2) the province’s energy structure should be transformed and upgraded by taking advantage of the “dual carbon” development model; and (3) regionally-differentiated carbon emission reduction policies should be developed.