Carbon Emission Trends Research Articles

Examining economic policy uncertainty's impact on environmental sustainability: Insights from nordic nations

The inherent unpredictability of economic policies can create a challenging environment for businesses and government. Such uncertainties may, in turn, have repercussions on environmental quality efforts. Accordingly, this study thoroughly explores the intricate relationships among several vital variables: economic policy uncertainty (EPU), economic growth, natural resource rent (NRR), renewable energy consumption (REC), and environmental quality. The geographical focus of this investigation is four Nordic economies (Sweden, Finland, Norway, and Denmark). The research method is firmly anchored in the STIRPAT framework, with a notable innovative twist. The CS-ARDL simulations estimate the long-term interaction between the series. One key advantage of the estimation model is the ability and robustness to handle reverse causality and endogeneity concerns in time-series econometric models. The empirical findings from this research demonstrate that EPU, economic growth, and NRR are primary indicators of increased CO2 emissions. These factors collectively contribute to the escalating trend of carbon emissions within the Nordic nations. In stark contrast, clean energy consumption emerges as a promising factor that not only counters the rising emissions trend but actively contributes to environmental quality. The importance of transitioning towards cleaner energy sources becomes evident in light of these findings. Policy-makers should recognize that economic policy uncertainty can be a potent driver of environmental pollution and should prioritize formulating transparent and consistent policies that attract investments in clean energy solutions.

Regional disparities, dynamic evolution, and spatial spillover effects of urban-rural carbon emission inequality in China

ObjectiveThis study recalculates the carbon emissions of urban and rural residents in China, analyzing the dynamic evolution trends of urban and rural carbon emissions. It explores the spatial spillover effects centered around the inequality in carbon emissions between urban and rural areas.MethodsThe study calculates the carbon emissions of urban and rural residents in each province based on the IPCC method. Non-parametric kernel density estimation is employed to depict the dynamic evolution characteristics of national, urban, and rural carbon emissions. The Theil Index is used to measure the disparities in urban and rural carbon emissions in major strategic regions, further applying the Theil Index to evaluate the inequality of urban and rural carbon emissions across provinces. This helps identify the driving factors affecting the inequality of urban and rural carbon emissions and their spatio-temporal effects.FindingCarbon emissions from urban and rural residents in China present a divergent development pattern. Urban emissions have increased, with inter-provincial disparities widening; rural emissions tend to stabilize, with slight growth in inter-provincial gaps. The overall inequality of carbon emissions in various regions of China experiences a three-phase journey of rise, decline, and stabilization. Urban inequality first increases then decreases, while rural inequality gradually lessens, showing clear regional and urban-rural differences. Market and government factors significantly impact the inequality of urban and rural carbon emissions. The development of the digital economy aids in reducing inequality and generates significant spatial spillover effects. The relationship between economic development level and carbon emission inequality is U-shaped. Industrial structure optimization can reduce urban-rural inequality, but its spatial spillover effect is not significant. Government intervention has limited effects, while environmental regulations may increase inequality. Opening up to the outside world helps reduce inequality, and the impact of population density is complex.

Applications of Fractional Order Logistic Grey Models for Carbon Emission Forecasting

In recent years, global attention to carbon emissions has increased, becoming one of the main drivers of global climate change. Accurate prediction of carbon emission trends in small and medium-sized countries and scientific regulation of carbon emissions can provide theoretical support and policy references for the effective and rational use of energy and the promotion of the coordinated development of energy, environment, and economy. This paper establishes a grey prediction model using the classical Logistic mathematical model in a determined environment to investigate the carbon emission system. At the same time, we use the basic principle of fractional-order accumulation to establish a grey prediction model with fractional-order Logistic and obtain the parameter estimation and time-response equation of the new model by solving the model through the theory related to fractional-order operators. The particle swarm optimization algorithm is used to complete the optimization process of the order of the fractional order grey prediction model and obtain the optimal model order. Then, the new model is applied to predict carbon emissions in five medium-emission countries: Ethiopia, Djibouti, Ghana, Belgium, and Austria. The new model shows better advantages in the validity analysis process, and the simulation results indicate that the new model proposed in this paper has stronger stability and better simulation and prediction accuracy than other comparative models, proving the model’s validity. Finally, the model is used to forecast the carbon emissions of these five countries for the five years of 2021–2025, and the results are analyzed, and relevant policy recommendations are made.

The temporal and spatial evolution of green finance and carbon emissions in the Pearl River Delta region: An analysis of impact pathways

The issue of carbon emissions has become increasingly urgent in tandem with the rapid growth of the economy in the Pearl River Delta region. This paper uses kernel density estimation and standard deviation ellipses based on Pearl River Delta data from 2012 to 2021 to explore the spatiotemporal evolution trends of green financing and carbon emissions. Additionally, it uses a spatial Durbin model to examine the spatial effects of green finance on carbon emissions. The results indicate the following: Firstly, over the years, green finance shows an overall increasing trend, with an internal spatial pattern characterized by higher levels in the northeast and lower levels in the southwest. In contrast, carbon emissions are decreasing, with an internal spatial pattern characterized by higher levels in the southwest and lower levels in the northeast. Secondly, more and more green financing initiatives are aiming towards the Pearl River Delta's northeast, suggesting a more significant concentration of such initiatives there than elsewhere. At the same time, carbon emissions seem to converge on the southwest of the Pearl River Delta, which could indicate that this area generally has greater carbon emission levels. Finally, the spatial effect results reveal that improving green finance levels can significantly inhibit carbon emission levels in the Pearl River Delta region. Evidence of spatial spillover effects indicates that the carbon emissions in neighboring areas are influenced to some extent. This study on green finance and carbon emissions' spatiotemporal characteristics and spatial impact gives a development path and theoretical support for sustainable development goals in the global economy.

Deep Learning-Based Carbon Emission Forecasting and Peak Carbon Pathways in China’s Logistics Industry

As a major energy-consuming industry, energy conservation and emission reduction in the logistics industry are critical to China’s timely achievement of its dual-carbon goals of “carbon peaking” by 2030 and “carbon neutrality” by 2060. Based on deep learning, Random Forest (RF) was used to screen out the key factors affecting carbon emissions in the logistics industry, and the Whale Algorithm-optimized Radial Basis Function Neural Network (WOA-RBF) was proposed. The Monte Carlo simulation predicted the future evolution trends of each key factor under the three scenarios of baseline scenario (BAU), policy regulation scenario (PR), and technological breakthrough scenario (TB) and accurately predicted the carbon emission trends of the logistics industry from 2023 to 2035 by using the most probable future values of each influencing factor as inputs to the WOA-RBF prediction model. The results of the study demonstrate that fixed asset investment (LFI), population (P), total energy consumption (E), energy consumption per unit of value added of the logistics industry (EIL), share of oil consumption (OR), and share of railway freight turnover (RTR) are the key factors influencing the logistics industry’s carbon emissions. Monte Carlo simulations can effectively reflect the uncertainty of future changes in these key factors. In comparison to the BAU and PR scenarios, the TB scenario, with the combined incentives of national policy regulation and technology innovation, is the most likely for the logistics industry to meet the “Peak Carbon” goal baseline scenario.

Predictive modeling of carbon emissions in Jiangsu Province's construction industry: An MEA-BP approach

Understanding and predicting carbon emissions in the construction industry is crucial for sustainable development planning. In this study, we employed the GM(1,1) model and the Mind Evolution Algorithm (MEA) to optimize the BP neural network model and forecast carbon emissions in Jiangsu Province from 2022 to 2026. Using data related to energy consumption and building material usage in the Jiangsu construction industry from 1995 to 2021, we estimated carbon emissions using the life cycle assessment method and IPCC carbon emission coefficient method. Additionally, we utilized the STIRPAT model and Principal Component Analysis (PCA) to select and reduce important variables, identifying three principal components. Subsequently, the MEA-BP model was employed to provide detailed forecasts of carbon emissions over the next five years, revealing a declining trend in carbon emissions from the construction industry. The study thoroughly analyzed the direct and indirect impacts of population growth, economic development, and industrial restructuring on carbon emissions, proposing targeted policy recommendations including sustainable urban planning and green building design. Future research could enhance the applicability and accuracy of the model by expanding the dataset and considering more uncertainty factors. This research offers valuable insights into the trends and influencing factors of carbon emissions in the construction industry of Jiangsu Province, providing guidance for policymakers and future research directions.

China contributed to low-carbon development: carbon emission increased but carbon intensity decreased

IntroductionPopulation expansion and economic development increased global greenhouse gas emissions, leading to serious environmental degradation. China, the world's largest developing country and promoter of the “Belt and Road Initiative” (BRI), accounts for 28.8% of the world"s total energy carbon emissions. How to reduce energy consumption to achieve the “double carbon” target (i.e., carbon peaking and carbon neutrality) and promote the implementation of Green BRI is still a serious challenge that China needs to face. MethodsWe evaluated China's carbon emissions using three indicators (i.e., total carbon emission, carbon intensity, and carbon emissions effect), and used spatial analysis to reveal the spatial and temporal trends of China's carbon emissions. In addition, the LMDI model was adopted to explore the driving mechanism of carbon emissions, so as to seek a path that can achieve harmonious economic and environmental development, as well as the “double carbon” target.ResultsChina's total carbon emission increased at a rate of 226.12% from 2000 to 2019, while the carbon intensity decreased at a rate of 48.84%. Carbon emission showed a trend of increasing and then decreasing from southwest to northeast. From 2000 to 2019, the total carbon emission, Gross Domestic Product (GDP), population size and total energy consumption are growing in synergy. Economic and population effects are positively related to carbon emissions, while technology effects are negatively related to it, indicating technological innovations contribute to the reduction of carbon emissions.DiscussionSome suggestions were proposed to control carbon emissions with a view to helping policy makers to formulate relevant policies. The findings provide a scientific basis and reference for the country to achieve the “double carbon” target and the low-carbon sustainable development of BRI.

China carbon emission accounts 2020-2021

In the past a few years, the outbreak of the COVID-19 epidemic has significantly changed global emission patterns and increased the challenges in emission reduction. However, a comprehensive analysis of the most recent trends of China's carbon emissions has not been conducted due to a lack of up-to-date emission accounts by regions and sectors. This study compiles the latest CO2 emission inventories for China and its 30 provinces during the epidemic (2020−2021), following the administrative-territorial approach from the International Panel on Climate Change (IPCC). Our inventories cover energy-related emissions from 17 types of fossil fuel combustion and cement production across 47 economic sectors. To provide a holistic view of emission patterns, we esitamted consumption-based emissions in China. We find that the COVID-19 epidemic led to a 50% reduction in the growth rate of territorial emissions in 2020 compared to 2019. This trend then reversed in 2021 as lockdown measures gradually relaxed. Our study reveals the impact of the rapid expansion of exports, driven by epidemic prevention materials and “stay-at-home economy” products on widening the differences between territorial- and consumption-based emissions. Our study offers a timely blueprint for designing strategies towards carbon peak and neutrality, especially in the context of sustainable recoveries and carbon mitigation post-pandemic.

The Impacts of Globalization, Economic Growth, Population and Urbanization on Carbon Dioxide Emissions in Afghanistan

The atmospheric carbon dioxide emissions have increased since last few years in Afghanistan. This increasing trend in carbon emissions may cause global warming, climate change and environmental pollution. Consequently, these indicated threats may suffer human life and ecological conditions in near future. Therefore, this study examines the impacts of globalization, economic growth, population and urbanization on carbon emissions in Afghanistan using annual time series data for the period 1990 – 2020. The study used the Augmented Dickey Fuller and Phillips–Perron unit root tests, the Breusch – Godfrey serial correlation Lagrange multiplier (LM) test and the autoregressive distributed lag (ARDL) bounds test to examine the short and long-run relationship of globalization, economic growth, population and urbanization with carbon emissions. The empirical results show that globalization, economic growth and population have a significant positive short and long-run relationship with carbon emissions. While urbanization has a significant short-run negative and long-run positive relationship with carbon emissions. Based on results, it is highly recommended that government should design environment friendly policies related to globalization, economic growth, population and urbanization to reduce environmental pollution in Afghanistan.

Medium and Long-term Carbon Emission Projections and Emission Reduction Potential Analysis of the Lingang Special Area Based on the LEAP Model

Based on the existing statistical data of the Lingang Special Area in Shanghai and considering its future socio-economic development， industrial structure， and technological development， a LEAP-Lingang model was developed to analyze the evolution trends of energy demand and carbon emissions under the baseline scenario， low-carbon scenario， and enhanced low-carbon scenario. To enhance the prediction accuracy of the model， the Logistic population growth model was used to predict future population data， and the learning curve model was used to simulate the cost evolution trend of related carbon reduction technologies. In addition， an economic evaluation model for carbon reduction technologies was developed， and the economic costs and emission reduction potential of typical carbon reduction technologies were evaluated by drawing a marginal emission reduction cost curve. The results showed that under the enhanced low-carbon scenario， the renewable energy accounted for 69% of the primary energy consumption， and the electric energy accounted for 91% of the terminal energy demand in 2060. The Lingang Special Area could achieve carbon peak by 2030， and the carbon emissions in 2060 were predicted to decrease by 94% compared to that in the baseline scenario. In terms of contribution to emission reduction， clean energy substitution， industrial structure optimization， and terminal energy efficiency improvement played a key role in reducing carbon emissions near the port. In the medium term （until 2035）， they were predicted to contribute 35.1%， 27.3%， and 16.2% of carbon emissions， respectively， and in the long term （until 2060）， they should contribute 50.6%， 8.75%， and 7.7% of carbon emissions， respectively. Regarding specific carbon reduction technologies， hydrogen power generation； water electrolysis for hydrogen； and carbon capture， utilization， and storage （CCUS） technology were of great significance for achieving net-zero emissions， but the costs of emission reduction were relatively high. The research results can provide ideas and references for the low-carbon and green development of the Lingang Special Area and related areas.

Digital economy and carbon emission: The coupling effects of the economy in Qinghai region of China

This study provides an in-depth analysis of the complex relationship between the digital economy and carbon emissions, fully drawing on essential principles of environmental economics, coupled economics, and sustainable development theory. Focusing on the Qinghai region in the western province of China, the study employs highly sophisticated methods such as multiple regression analysis and system dynamics modeling to reveal the multidimensional coupling effects between digital economy development and carbon emission dynamics. The study's results clearly show that in the Qinghai region of China, the booming growth of the digital economy is related to carbon emissions. Of particular interest, the study finds that this relationship exhibits a high degree of complexity and non-linearity and evolves gradually over time. Initially, the rapid expansion of the digital economy, accompanied by high energy consumption and increased carbon emissions, posed a significant challenge to environmental protection. However, a clear inverted “U"-shaped relationship has emerged as the digital economy evolves. This key inflection point signals a shift in the landscape as the digital economy begins to deliver some ecological benefits, potentially reducing the trend of carbon emissions in the future. The findings of this study go beyond simple causality and reveal a complex and evolving dynamic relationship between the digital economy and carbon emissions. Through such insights, this study provides a solid academic foundation and carefully constructs actionable policy recommendations to drive sustainable development. These insights apply to the Qinghai region of China and provide valuable references and lessons for other areas facing similar challenges.

An evaluation concentrated on post-peak carbon trend scenarios designing and carbon neutral pathways in Hebei Province, China

China has committed to peak carbon emissions by 2030 and become carbon neutral by 2060. Hebei, as a major energy province in China, has more challenging carbon peaking and carbon neutrality targets. Based on the extended STIRPAT model, this paper evaluates the time to peak carbon and post-peak carbon trend changes in Hebei province under different scenarios from 2020 to 2060. Based on the support vector machine model with particle swarm optimization, this paper evaluates the carbon neutral pathways under different scenarios in Hebei province from 2020 to 2060 in terms of forests, croplands, wetlands, grasslands and Carbon Capture, Utilization and Storage (CCUS). The results show that a monotonically flat trend without inflection point, a single-inflection-point plateau-period trend and a multi-inflection-point inverted U-wave trend occur after carbon peaks. Hebei Province can achieve carbon neutrality by 2060 under the policy scenario and the enhanced scenario, and the enhanced scenario is better. Comparing with existing studies, the innovation of this paper is to design different trends of changes after carbon peak, combining carbon absorption and carbon emission, and providing more diversified development paths for carbon neutral strategies through a more integrated perspective and multi-scenario comprehensive analysis. Therefore, In the future, Hebei Province should establish a platform for carbon emissions monitoring and carbon footprint assessment, and set up an early warning and assessment mechanism for carbon peaking, so as to prevent a rebound trend in carbon emissions after peaking. At the same time, it should introduce and develop carbon capture and conversion technologies and promote the low-carbon transformation of the industrial and energy sectors, thereby forcing the early realization of peak carbon. Pay attention to the role of forestry resources in carbon neutrality, increase carbon sink resources such as forests and wetlands, promote the development of CCUS, and enhance the implementation of the policy.

Ecological and economic influencing factors on the spatial and temporal evolution of carbon balance zoning in the Taihu Basin

The escalation in carbon dioxide concentration has precipitated global climate warming, accentuating ecological and environmental concerns. Notably, China stands as the world’s largest carbon emitter, with the Taihu Lake basin emerging as a carbon-intensive region within the country. This paper undertakes a comprehensive analysis spanning 2005 to 2020, calculating the economic contribution coefficient of carbon emissions and the ecological carrying coefficient of carbon absorption in the Taihu Lake basin. The study includes a delineation of carbon balance zones and an exploration of the geographical and spatial influences of both ecosystem and economic factors. The overarching trend in carbon emissions within the Taihu Lake Basin initially exhibited rapid growth, followed by a fluctuating decline, with the pivotal year being 2012, recording the apex of emissions at 575.8293 million tons. Concurrently, total carbon absorption demonstrated a fluctuating growth trajectory, ascending from 82.3503 million tons in 2005 to 85.6488 million tons in 2020. The carbon emission intensity in the basin manifested a pattern of high concentration in the northeast and low concentration in the southwest, while the carbon absorption intensity displayed the inverse pattern. The carbon balance across the Taihu Lake Basin revealed a spatial incongruity, characterized by a suboptimal pattern in the northeast and a favorable pattern in the southwest. Zhejiang Province emerged as an ecological stronghold within the basin, acting as the primary carbon sink functional area. Urban built-up areas and forested regions emerged as principal influencers of carbon balance in the Taihu Lake basin. Urban construction land, population density, and arable land area were identified as primary contributors to carbon emissions, whereas per capita GDP, forests, grasslands, and water bodies were identified as main contributors to carbon absorption in the watershed.

Simulation of fishery energy consumption and carbon emissions in Zhoushan City based on the long-range energy alternatives planning system model

To understand the current situation and development trend of energy consumption and carbon emissions in the fishery industry, a Long-Range Energy Alternatives Planning System (LEAP)-fishery model for Zhoushan City was constructed, and the trends of energy consumption and carbon emissions in the Zhoushan City fishery industry were analyzed under the business-as-usual scenario, the low-carbon scenario, and the strengthened low-carbon scenario. The results show that under the business-as-usual scenario, the future energy consumption of Zhoushan City’s fisheries in general shows an increasing trend, and the composition of energy consumption in all scenarios is primarily dominated by fossil energy, with diesel and coal predominating. In terms of the structure of end-use energy consumption, fishing vessels and aquaculture consume the most energy, with energy consumption exceeding 30% of total energy consumption in all scenarios. In terms of the emission reduction contribution rate, which the promotion of clean energy generation, the improvement of energy efficiency, and the reduction of aquaculture production play key roles in the low-carbon development of the fishery industry. From an economic perspective, carbon reduction measures such as reduction of aquaculture production and increased efficiency of energy delivery would have significant economic benefits, but these measures have limitations. Finally, suggestions are made to optimize the energy structure, improve the implementation of energy intensity and total energy control, and strengthen public awareness of energy conservation to reduce carbon emissions from the fishery industry, thus making the research in this paper relevant to real-world situations.

An analysis and prediction of carbon emissions in the sphere of consumer lifestyles in Beijing.

In alignment with China's "dual carbon" goals and its quest to build an ecological civilization, this study scrutinizes the carbon emissions derived from consumer lifestyles, with a particular focus on Beijing, a high-consumption urban metropolis. Utilizing the expanded STIRPAT model and ridge regression, factors such as permanent population, per capita consumption expenditure, energy intensity, energy structure, and consumption structure are examined to evaluate their impact on lifestyle-associated carbon emissions. A scenario analysis is also conducted to project future carbon emissions in Beijing. From 2010 to 2020, there was an overall upward trend in lifestyle-associated carbon emissions, up to a maximum of 87.8260 million tons. Indirect consumption-related carbon emissions, particularly those associated with residential and transportation-related consumption, constituted the primary sources. The most influential factors on carbon emissions were found to be the consumption structure. Notably, adopting a low-carbon consumption mindset and an optimized consumption structure could foster significant carbon reduction. Projections suggest that by 2035, carbon emissions due to residents' consumption could decline by 39.72% under a low-carbon consumption scenario and by 48.74% under a coordinated development scenario. Future efforts should prioritize promoting green, low-carbon living, refining consumption structure and practices, curbing excessive housing consumption, improving energy structure, and raising technological and energy efficiency standards.

A new multiregional carbon emissions forecasting model based on a multivariable information fusion mechanism and hybrid spatiotemporal graph convolution network

Developing scientific and effective carbon emissions reduction policies relies heavily on precise carbon emission trend prediction. The existing complex spatiotemporal correlation and diverse range of influencing factors associated with multi-regional carbon emissions pose significant challenges to accurately modeling these trends. Under this constraint, this study is inspired by graph learning to establish a hybrid dynamic and static graph-based regional carbon emission network framework, which introduces a novel research standpoint for investigating short-term carbon emissions prediction (CEP). Specifically, a parallel framework of attribute-augmented dynamic multi-modal graph convolutional neural networks (ADMGCN) and temporal convolutional networks with adaptive fusion multi-scale receptive fields (AFMRFTCN) is proposed. The proposed model is evaluated against nineteen state-of-the-art models using daily carbon emission data from 30 regions in China, demonstrating its effectiveness in accurately predicting the trends of multi-regional carbon emissions. Conclusions are drawn as follows: First, especially in regions with marked periodicity, compared with the best baseline model, the mean absolute percentage error (MAPE) of our model is reduced by 20.19%. Second, incorporating graph convolutional neural networks (GCNs) with dynamic and static graphs is advantageous in extracting the spatial features of China's carbon emission network, which are influenced by geographical, economic, and industrial factors. Third, the parallel ADMGCN-AFMRFTCNs framework effectively captures the influence of external information on carbon emissions while mitigating the issue of low prediction accuracy resulting from univariate information. Fourth, the analysis reveals significant differences in the short-term (30-day) growth rate of carbon emissions among different regions. For example, Henan exhibits the highest growth rate (37.38%), while Guizhou has the lowest growth rate (−7.46%). It is valuable for policymakers and stakeholders seeking to identify regions with distinct emission patterns and prioritize mitigation efforts accordingly.

Carbon Emission Accounting during the Construction of Typical 500 kV Power Transmissions and Substations Using the Carbon Emission Factor Approach

Carbon emission calculation during power transmission and substation construction provides valuable insights into the trend of carbon emissions and the development of low-carbon power grids. In this regard, this study divides the power transmission and substation construction process into production, transportation, and construction stages based on the sources of carbon emissions and employs a life cycle assessment to calculate the total carbon emissions using the carbon emission factor method for typical 500 kV projects. The results show that in the construction process the production stage contributes the most carbon emissions, with material and equipment production for power transmission accounting for 78% and 14% of the total emissions, respectively. The transportation and construction stage contribute 1% and 7% of the total emissions, respectively. For substations, material and equipment production contribute 67% and 30% of the total emissions, respectively. The transportation and construction phases contribute 1% and 2% of the total emissions. Through the qualitative and quantitative analysis of the carbon emission results, the construction scale and the topography and geology have significant impacts on carbon emissions from power transmission and substation projects. Finally, some targeted recommendations for carbon emission reduction for power transmissions and substations are proposed based on the influencing factors of each stage of the construction.

A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement

Greenhouse gas emissions are widely recognized as the primary cause of global warming, leading to a growing attention on carbon emission management. However, the existing studies still failed to propose a feasible approach to directly forecast carbon emission trends and also did not take into account both environmental regulation and efficiency improvement. Hence, this study aims to propose a novel carbon emission trend forecast model based on data-driven rule-base with considering the intensity coefficient of environmental regulation and the management efficiency of carbon emissions. Carbon emission data of 30 Chinese provinces are collected to illustrate the effectiveness of the proposed model. Results indicated that: 1) the data-driven rule-base model is able to directly forecast carbon emission trends within range from −18.54 % to 19.18 %; 2) by integrating regulation intensity, the predicted results of the model have smaller carbon emission tends, e.g., decrease of average changing rate from 0.4100 to 0.2762; 3) by further integrating efficiency improvement, the predicted results align more with the expected objectives of policy makers, i.e., the average carbon emission efficiency approximates 0.8920 and the number of provinces being effective efficiency is increased to 8. These findings also highlighted the importance of carbon emission tend forecast with environmental regulation and efficiency improvement. The proposed carbon emission trend forecast model could serve as an alternative tool for achieving dual carbon goals in the context of China.

Carbon emission scenario prediction for highway construction projects

In order to carry out the research on the measurement and prediction of carbon emissions from highway projects, the prediction index system is constructed by selecting seven influencing factors, namely, urbanization rate, industrial structure, energy intensity, energy structure, key low-carbon technologies, highway construction planning, and green financial support. The carbon emission coefficient method is used to measure the carbon emissions of highway projects from the perspective of energy consumption, and finally, a carbon emission scenario prediction model based on the scenario analysis method and the LSTM algorithm of the recurrent neural network is constructed to put forward a new way of predicting the carbon emissions of highway projects. Taking Hunan province as a case study object, the carbon emission trends in the next 15 years are predicted under three scenarios: low carbon, baseline, and high carbon. The results show that the highway project in Hunan province is expected to peak in 2031 under the low-carbon scenario, in 2037 under the baseline scenario, and around 2044 under the high-carbon scenario. It is also found that energy saving and emission reduction measures have a certain lag in the emission reduction effect of highway projects. Finally, based on this, suggestions and countermeasures for carbon emission reduction of highway development in Hunan province are proposed.

Net-zero energy transition in ASEAN countries: The evolutionary model brings novel perspectives to the cooperative mechanism of climate governance

In an era marked by escalating climate change, the fragile ecological balance faces increasing strain. Whilst significant knowledge exists regarding the accumulation of carbon emission within the Association of Southeast Asian Nations, little is known about when and how countries could reach net-zero emission goal as agreed in Paris Agreement. For this purpose, our study examines the primary driving factors of carbon emission from 1990 to 2020 across the ten countries using the Logarithmic Mean Divisia Index model. We leverage the random forest model to explore the net-zero scenarios and the Autoregressive Integrated Moving Average approach to identify the evolutionary trajectories of carbon emission trends. Our findings underscore the imperative need for expediting decarbonization efforts, emphasizing the urgency for widespread adoption of clean technologies and substantial investment in green initiatives. Countries at similar stages of progress might establish a cooperation mechanism of clean energy base construction, energy storage allocation and policy formulation. These insights can help us better estimate future demand of clean energy, explore strategies for decarbonization, and inform historical commonalities of carbon emission growth.