Carbon Emission Trends Research Articles

Toward carbon peaking and neutralization: The heterogeneous stochastic convergence of CO2 emissions and the role of digital inclusive finance

This article explores the dynamic distribution of city-level CO2 emissions from China's national and regional perspectives. For this, we apply a q-σ stochastic convergence model to inspect the heterogeneous convergence trend of carbon emissions at different quantiles. A panel time-varying quantile regression model with a factor structure was employed to determine the absolute and conditional q-β stochastic convergence of CO2 emissions and to examine the dynamic influence of digital inclusive finance on the convergence of carbon emissions. The results show (i) CO2 emissions exhibit a spatially imbalanced distribution pattern of being low in east and high in the west. The dynamic progression of carbon emissions in different areas presents huge differences in which Eastern, Central and Western China show insignificant and significant changes of distribution trending in CO2 emissions. (ii) Excluding the central region, country's CO2 emissions in eastern and western regions display dynamic q-σ convergence and dynamic absolute q-β convergence at different quantiles. In contrast, a conditional q-β convergence of CO2 emissions in the whole country and each region is found across 5%–95% quantiles. (iii) At national and regional emissions, the dynamic q-σ convergence, absolute and conditional q-β convergence typically becomes more pronounced at mid-high quantile than at low quantile. (iv) Development of digital inclusive finance across the country and in Central and Western China generally facilitates carbon emissions convergence, but the opposite result for the eastern region. Finally, comprehensive finance growth, digital finance and low-carbon technological efforts can be a better fit for achieving dual-carbon goals.

Research on Influencing Factors of Residential Building Carbon Emissions and Carbon Peak: A Case of Henan Province in China

Buildings are considered to have significant emission reduction potential. Residential building carbon emissions, as the most significant type of building-related carbon emissions, represent a crucial factor in achieving both carbon peak and carbon neutrality targets for China. Based on carbon emission data from Henan Province, a large province located in central China, between 2010 and 2020, this study employed the Kaya-LMDI decomposition method to analyze seven driving factors of carbon emission evolution, encompassing energy, population, and income, and assessed the historical reduction in CO2 emissions from residential buildings. Then, by integrating Kaya identity static analysis with Monte Carlo dynamic simulation, various scenarios were established to infer the future evolution trend, peak time, and potential for carbon emission reduction in residential buildings. The analysis results are as follows: (1) The carbon emissions of residential buildings in Henan exhibited a rising trend from 2010 to 2020, albeit with a decelerating growth rate. (2) Per capita household disposable income is the main driving factor for the increase in carbon emissions, but the household housing purchase index inhibits most of the growth of carbon emissions for the residential buildings in Henan, with the total carbon emission reduction of residential buildings reaches 106.42 million tons of CO2 during the research period. (3) During the period from 2020 to 2050, residential buildings in Henan Province will exhibit an “inverted U-shaped” trend in carbon emissions under the three static scenarios. The base scenario predicts that carbon emissions will reach their peak of 131.66 million tons in 2036, while the low-carbon scenario forecasts a peak of 998.8 million tons in 2030 and the high-carbon scenario projects a peak of 138.65 million tonnes in 2041. (4) Under the dynamic simulation scenario, it is anticipated that residential buildings in Henan Province will reach their carbon peak in 2036 ± 3 years, with a corresponding carbon emission of 155.34 million tons. This study can serve as a valuable reference for the future development of low-carbon pathways within the building sector.

Carbon emission reduction analysis for cloud computing industry: Can carbon emissions trading and technology innovation help?

The emerging cloud computing industry (CCI) has become an important source of carbon emissions; therefore, choosing an appropriate carbon emission reduction strategy is of key importance. Based on two important alternative carbon reduction strategies, namely carbon emissions trading (CET) and emission reduction technological innovation (ERTI), in this paper, a scenario-based simulation model of carbon emission reduction strategies is established for the CCI of China. Based on different derivative scenarios of CET and ERTI strategies, 22 carbon reduction strategy scenarios are designed and subjected to simulation analysis. Then, a slacks-based model that considers undesirable output is constructed and used to evaluate the comprehensive effect of proposed carbon reduction strategy scenarios. Based on the evaluation results of the strategy scenarios, a tolerant CET strategy scenario with a high free quota ratio and a low carbon trading price has the potential to reverse the increasing trend of carbon emissions. This tolerant strategy scenario can also achieve a more efficient carbon emission reduction result for the CCI in the long run. In terms of technology innovation, the strong promotion of installed zero‑carbon capacity and negative emission technology applications can effectively promote the reduction of carbon emissions. The results of this analysis show that the carbon emission reduction strategy with tolerant CET and strong ERTI will contribute to the healthy development of the CCI.

Feasibility of Remanufacturing in Shoemaking Machines under the Trend of Net-zero Carbon Emissions

Achieving net-zero carbon emissions by 2050 has become a common goal in the world. An effective strategy to reduce carbon emissions will be the key to maintaining international competitiveness. Although green energy exchange is mature around the world, the relevant systems and regulations in Taiwan are not yet ready. This research examines the feasibility of shoemaking machines remanufacturing and tries to seek effective strategies to achieve carbon neutrality for the original equipment manufacturers (OEMs) of shoemaking machines. The evaluation of remanufacturing in a shoemaking machine is based on imprecise and fuzzy information. First, the feasibility evaluation model of remanufacturing in a shoemaking machine is established, including technical, economic, and resource environment feasibility criteria. Second, the comprehensive benefit evaluation model of the remanufacturing shoemaking machine is established, in which the weight of each criterion is determined by the Analytic Hierarchy Process (AHP). Finally, combined with the questionnaire, the evaluation method is verified and analyzed. The results show that the four criteria (clusters) for remanufacturing shoemaking machines have different weights, in descending order: Product design, Business model, Recycling system, and Corporate image. This implies that Product design is the most important factor for remanufacturing shoemaking machines, followed by the Business model, Recycling system, and Corporate image. Therefore, to succeed in the circular economy, OEMs need to rethink how to redesign their products from the beginning and create a new business model.

Prediction of CO2 emissions in China by generalized regression neural network optimized with fruit fly optimization algorithm.

As global warming becomes more prominent, the need to reduce carbon emissions to achieve China's carbon peak target is increasing. It is imperative to seek effective methods to predict carbon emissions and propose targeted emission reduction measures. In this paper, a comprehensive model integrating grey relational analysis (GRA), generalized regression neural network (GRNN) and fruit fly optimization algorithm (FOA) is constructed with carbon emission prediction as the research objective. Firstly, GRA is used for feature selection to find out the factors that have a strong influence on carbon emissions. Secondly, the parameter of GRNN is optimized using FOA algorithm to improve the prediction accuracy. The results show that (1) fossil energy consumption, population, urbanization rate and GDP are important factors affecting carbon emissions; (2) FOA-GRNN outperforms GRNN and back propagation neural network (BPNN), verifying the effectiveness of FOA-GRNN model for CO2 emission prediction. Finally, by analyzing the key influencing factors and combining scenario analysis with forecasting algorithms, the carbon emission trends in China for 2020-2035 are forecasted. The results can provide guidance for policy makers to set reasonable carbon emission reduction targets and adopt corresponding energy saving and emission reduction measures.

Spatial-Temporal Characteristics and Influencing Factors of Carbon Emissions from Land Use and Land Cover in Black Soil Region of Northeast China Based on LMDI Simulation

Land use change accounts for a large proportion of the carbon emissions produced each year, especially in highly developed traditional heavy industry and agriculture areas. In this study, we estimated the carbon emissions from land use in the Black Soil Region of Northeast China (BSRNC) from 1990 to 2020. We utilized seven periods of land use remote sensing image data spanning the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020, with a 30-m grid resolution. Additionally, socio-economic data was incorporated into the analysis. The preprocessing of the remote sensing images involved several steps using ENVI 5.5, including radiometric correction, fusion, mosaic, and cropping. The land types were classified into six major categories: cropland, forest land, grassland, water area, construction land, and unused land, using the LUCC classification system. The IPCC coefficient method was used to calculate the trends in carbon emissions from land use, and the logarithmic mean Divisia index (LMDI) method was applied to analyze the influencing factors. The main conclusions are as follows: (1) From 1990 to 2020, the net carbon emissions from land use in the BSRNC increased from 11.91 × 104 t to 253.29 × 104 t, with an annual growth rate of 8.04%. (2) Spatially, land use carbon emissions exhibited an agglomeration pattern that gradually weakened and the regional emission differences gradually narrowed. (3) Income level was identified as the most important factor influencing land use carbon emissions in the BSRNC from 1990 to 2020. Land use efficiency had a inhibitory effect on net carbon emissions, reducing land use carbon emissions by 1730.63 × 104 t.

Implication of machine learning techniques to forecast the electricity price and carbon emission: Evidence from a hot region

The current study examines the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price accurately. The main contribution is focused on eastern region of Saudi Arabia, a relatively hottest geographical area full of energy resources but with different electricity consumption patterns. The relative irrelevance of temperature as predicting factor of electricity consumption is quite surprising and contradicts the previous studies. In the eastern region, electricity price has negative association with electricity consumption. While comparing traditional and machine learning, it is found that machine learning techniques offer better predictability. Amongst the machine learning techniques, the support vector machine has the lowest errors in forecasting the electricity price. Additionally, the support vector machine approach is used to forecast the trend of carbon emissions caused by electricity consumption. The findings have policy implications and offer valuable suggestions to policymakers while addressing the determinants of electricity consumption and forecasting electricity prices.

Numerical investigation on energy efficiency and pollutant emission of a green double-chambered cremator

Cremation is the main approach to disposing of corpses as a traditional burial substitute. With the increasing number of deaths from the aging society, the growing trend of hazard pollutant and carbon emissions from crematories requires immediate actions to promote upgrades to existing cremators. However, a comprehensive optimizing of crematories is challenging because energy conservation and reduction of HAPs conflictly require reducing and increasing the combustion intensity, respectively. To solve the dilemma, this study developed a 3-D numerical model for the combustion and heat transfer processes in a double-chambered cremator. The model was validated by the experimental data extracted from an in-operation crematoria plant. The effect of cremator structure, fuel usage, and air–fuel ratio on velocity and temperature distributions in the chambers were quantitatively evaluated to satisfy the requirements for the energy consumption and the pollutant reduction. Up to 60% of fuel usage reduction was achieved by optimizing the inlet conditions compared to the conventional cremators. A clearly decline in combustion intensity was observed with a decreasing body fat percentage. Adopting an inter-chamber conduction layer helped to transport a maximum of 3079 kJ heat from the primary chamber to the secondary chamber, which contributed to the energy conservation for the green cremator.

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China.

As the largest energy consumer, China's control of carbon emissions from energy consumption plays a pivotal role in world climate governance. However, few studies have been conducted to explore the emission reduction pathways that promote a high level of synergy between China's economic growth and the " carbon peaking and carbon neutrality " goal from the perspective of energy consumption. Based on the measurement of energy consumption carbon emissions, this paper reveals the spatial and temporal distribution and evolution trends of carbon emissions in China at the national-provincial level. The multi-dimensional socio-economic factors such as R&D and urbanization are taken into account, and the LMDI model is used to decompose the driving effects of energy consumption carbon emissions at the national-provincial levels. Further, this paper combines the Tapio decoupling index with the LMDI model to decompose the decoupling states of China year by year and at the provincial level in four periods to explore the reasons for the change of carbon decoupling states. The results show that: (1) China's energy consumption carbon emissions grew at a high rate before 2013, and slowed down after that. There are significant differences in the scale and growth rate of carbon emissions among provinces, which can be classified into four types accordingly. (2) The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect inhibit the growth of China's carbon emissions. (3) Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. According to the conclusions, this paper proposes targeted policy recommendations based on China's energy endowment.

Scenario analysis of transportation carbon emissions in China based on machine learning and deep neural network models

Coping with the relation between the increase in carbon emissions and energy consumption in the transportation sector is a pressing issue today. Machine learning and deep neural networks were used in this study to explore the influential factors and trends in future transportation carbon emissions. First, the least absolute shrinkage and selection operator (LASSO) regression was adopted to screen out the key influencing factors in transportation carbon emissions. Second, the prediction performance of the long short-term memory (LSTM) network, generalized regress neural network, and back propagation (BP) network were compared, and an improved LSTM optimized by the sparrow search algorithm was proposed. Third, LASSO-SSA-LSTM was used to predict the transportation sector’s future carbon emissions trends under different scenarios. The results suggested that transportation carbon emissions in China presented a trend of ‘rapid increase—fluctuating decrease—continuous increase’ from 2010 to 2019. Although the main determinant in curbing the rising rate of carbon emissions effectively is the continuous development of renewable energy technology, the variation in transportation carbon emissions in China under eight scenarios showed significant differences. Generally, systemic changes and innovations are crucial to accommodate China’s future low-carbon and sustainable transportation development.

An integrated view of correlated emissions of greenhouse gases and air pollutants in China

BackgroundAir pollution in China has raised great concerns due to its adverse effects on air quality, human health, and climate. Emissions of air pollutants (APs) are inherently linked with CO2 emissions through fossil-energy consumption. Knowledge of the characteristics of APs and CO2 emissions and their relationships is fundamentally important in the pursuit of co-benefits in addressing air quality and climate issues in China. However, the linkages and interactions between APs and CO2 in China are not well understood.ResultsHere, we conducted an ensemble study of six bottom-up inventories to identify the underlying drivers of APs and CO2 emissions growth and to explore their linkages in China. The results showed that, during 1980–2015, the power and industry sectors contributed 61–79% to China’s overall emissions of CO2, NOx, and SO2. In addition, the residential and industrial sectors were large emitters (77–85%) of PM10, PM2.5, CO, BC, and OC. The emissions of CH4, N2O and NH3 were dominated by the agriculture sector (46–82%) during 1980–2015, while the share of CH4 emissions in the energy sector increased since 2010. During 1980–2015, APs and greenhouse gases (GHGs) emissions from residential sources generally decreased over time, while the transportation sector increased its impact on recent emissions, particularly for NOx and NMVOC. Since implementation of stringent pollution control measures and accompanying technological improvements in 2013, China has effectively limited pollution emissions (e.g., growth rates of –10% per year for PM and –20% for SO2) and slowed down the increasing trend of carbon emissions from the power and industrial sectors. We also found that areas with high emissions of CO, NOx, NMVOC, and SO2 also emitted large amounts of CO2, which demonstrates the possible common sources of APs and GHGs. Moreover, we found significant correlations between CO2 and APs (e.g., NOx, CO, SO2, and PM) emissions in the top 5% high-emitting grid cells, with more than 60% common grid cells during 2010–2015.ConclusionsWe found significant correlation in spatial and temporal aspects for CO2, and NOx, CO, SO2, and PM emissions in China. We targeted sectorial and spatial APs and GHGs emission hot-spots, which help for management and policy-making of collaborative reductions of them. This comprehensive analysis over 6 datasets improves our understanding of APs and GHGs emissions in China during the period of rapid industrialization from 1980 to 2015. This study helps elucidate the linkages between APs and CO2 from an integrated perspective, and provides insights for future synergistic emissions reduction.

GGDP that Change the Climate

In this paper, we use the Entropy Weight Method (EWM) to calculate the weights and TOPSIS Algorithm to calculate the score. Then, we made a preliminary comparison between the results of the two systems. Then, in order to obtain precise relation, we use Grey Correlation Analysis to reflect the correlation between variables. By comparing the grey correlation coefficient between different years and different countries, it is concluded that the correlation between GGDP and climate is higher than GDP. Due to the nature of cross-sectional data, we introduce time series data for the exploration of long-term trend. Through establishing the Grey System Prediction GM (1,1) model based on the grey system theory, we forecast GDP and GGDP of different countries from 2020 to 2024 which is based on the data from 2010-2019. We use multiple linear regression to predict carbon emissions, and present the results with the change trend of carbon emissions.

Dynamic Prediction and Driving Factors of Carbon Emission in Beijing, China, under Carbon Neutrality Targets

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development.

Development criterion of estimating hourly global solar radiation for all sky conditions in China

The high quality hourly global solar radiation data becomes more and more important under the trend of low building energy consumption and carbon emission. The models based on daily global solar radiation and hourly meteorological parameters are two kinds of efficient methods to compensate for measured data deficiency. However, the applicable climate and sky conditions of two models are needed to be clarified. Based on 45 stations in China, the coupling correlations between hourly global solar radiation and meteorological parameters are obtained by correlation analysis. The eight decomposition models and four models based on the main hourly meteorological parameters are developed and evaluated for climates and sky conditions using the mean absolute bias error, relative root mean squared error, and Nash-Sutcliffe efficiency coefficient. Considering the advantages of two kinds of models, the criterion of estimating hourly global solar radiation data is developed. The results show that hourly sunshine duration and hourly surface temperature are the main factors reflecting the change of hourly global solar radiation. In humid and sub-humid climates, the hourly relative humidity can further characterize the fluctuation of hourly global solar radiation. CPR model performs best among decomposition models, especially in sunny sky conditions. The meteorological parameters models own higher accuracy in cloudy conditions. In regions with hourly sunshine duration measurement, CPR model is recommended when daily clearness index is higher than 0.7 and lower than 0.2. When daily clearness index varies from 0.2 to 0.7, the model should be established by selecting hourly sunshine duration and hourly surface temperature in arid and semi-arid climates and hourly sunshine duration, hourly surface temperature and hourly relative humidity in humid and subhumid climates, respectively. In regions without hourly sunshine duration measurement, CPR model should be used regardless of weather conditions and climate types.

Application of shared socioeconomic pathways at the subnational level: carbon emission forecasts in Qinghai Province, China, integrating socioeconomic models

Forecasting carbon dioxide (CO2) emissions is necessary to estimate their climate impact and the required mitigation efforts. However, existing scenario frameworks are not consistent and limit the comparability of projection studies. In this study, the “Stochastic Impacts by Regression on Population, Affluence and Technology” model was combined with the shared socioeconomic pathways (SSPs) defined by the Intergovernmental Panel on Climate Change. Model forecasts were calculated for each SSP from 2020 to 2060 in Qinghai Province, an underdeveloped, carbon-sink-rich region in China. Total CO2 emissions in Qinghai Province consistently exhibited an upward then downward trend, with large differences between SSPs in the peak date and maximum value of total CO2 emissions. Considering the current socioeconomic development status and total carbon emission trends in Qinghai Province, SSP1 and SSP2 were the most suitable pathways, providing balance between carbon emissions reduction and population well-being.

The Effect of Service Quality of Green Restaurants on Green Restaurant Image and Revisit Intention: The Case of Istanbul

With the rise of environmental awareness, trends of avoiding food waste and reducing carbon emissions trends have generated green business opportunities for the food and beverage industry. In this industry, the competitive advantage provided by high service quality is an important factor. In this context, the effects of perceived service quality on green restaurants’ image and customers’ revisit intentions were investigated. Moreover, the effects of green restaurants’ image on revisit intention were discussed. The GRSERV scale was used to measure the service quality of green restaurants in İstanbul; a total of 356 questionnaires were collected. Partial least squares (PLS) path analysis showed that the perceived service quality by customers visiting green restaurant businesses has a positive impact on both green restaurant image and revisit intention. It has also been determined that green restaurant image has significant effects on revisit intention.

Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysis

The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. As the core province in central China, Hubei Province is under prominent pressure of carbon emission reduction. In this paper, the future development trend of carbon emissions is analyzed, and the emission peak value and carbon peak time in Hubei Province is predicted. Firstly, the generalized Divisia index method (GDIM) model is proposed to determine the main influencing factors of carbon emissions in Hubei Province. Secondly, based on the main influencing factors identified, a novel STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) extended model with ridge regression is established to predict carbon emissions. Thirdly, the scenario analysis method is used to set the variables of the STIRPAT extended model and to predict the emission peak value and carbon peak time in Hubei Province. The results show that Hubei Province's carbon emissions peaked first in 2025, with a peak value of 361.81 million tons. Finally, according to the prediction results, the corresponding suggestions on carbon emission reduction are provided in three aspects of industrial structure, energy structure, and urbanization, so as to help government establish a green, low-carbon, and circular development economic system and achieve the industry's cleaner production and sustainable development of society.

Carbon peak forecast and low carbon policy choice of transportation industry in China: scenario prediction based on STIRPAT model.

As the second largest CO2 emission department, transportation industry's carbon peak and carbon reduction are very important for China to smoothly achieve carbon peak by 2030 and carbon neutrality by 2060. This paper analyzes the influencing factors from the perspectives of population, economy, technology and transportation equipment structure, subdivides 20 scenarios to predict the carbon emissions of the transportation industry of the whole China and various regions based on scenario analysis method, explores the carbon peak path, and puts forward corresponding policy recommendations. The study found that (1) from the overall trend of carbon emissions, the total carbon emissions of China's transportation industry showed an overall upward trend from 2010 to 2019 while the growth rate of carbon emissions showed a downward trend. (2) From the perspective of influencing factors, population size, urbanization rate, economic scale, traffic development, traffic carbon intensity, and highway mileage have positive effect on the growth rate of China's transportation CO2 emissions. The increase in the proportion of energy structure and railway cargo turnover has the negative effect on carbon emissions in the transportation industry. (3) From the prediction results at the national level, technological breakthroughs have a limited effect on carbon emission reduction in China's transportation industry, while structural equipment optimization has the most significant effect on its emission reduction. When technological breakthroughs and equipment structure optimization are carried out simultaneously, the carbon emission reduction effect is the best. The carbon peak of China's transportation industry would achieve as early as 2030, with a peak range of 70,355.54-84,136.17 million tons. (4) From the perspective of prediction results at the regional level, the provinces with rapid population growth and per capita GDP growth, the provinces with rapid population growth and per capita GDP growth, and the provinces with low population growth and per capita GDP growth should control their average annual growth rate of carbon emissions of the transportation industry to 1.13%, 0.72% and 0.58% respectively in 2019-2030, in order to ensure the achievements of the carbon peak target.

The Correlation of Various Economic and Political Variables on Carbon Emissions

With the sea levels and surface temperatures steadily rising, many scientists have warned of an oncoming global climate crisis. Currently, most studies point towards greenhouse gas emissions as being the main contributor for this climate crisis. In this study, we will investigate the correlation between various political and economic factors with carbon emissions. We run correlations among GDP and environmental policy stringency with the carbon dioxide emissions per capita to see the way the trends change over the scales of these variables. We also observe the trends of total population and total carbon emissions over time to analyze any occurring patterns. The goal of finding the correlations between these factors is that they may shed light on other potential elements that are commonly overlooked when attempting to minimize the global climate crisis. Our analysis also shows some inconsistencies between the intended and actual effects of the policy.

Carbon Emission in Malaysia: Trends and Initiatives of Government

Malaysia is one of the world's most vulnerable countries to global warming and climate change, mainly brought on by the release of greenhouse gases (GHG) from the burning of fossil fuels (Knoema, 2021). In 2015, Malaysia committed to the Paris Climate Accord, working with the rest of the world to reduce carbon emissions and keep the rise in temperature to 1.5 °C. The increased use of electricity, transportation, and the growing rate of industrialization are among the factors that lead to a higher temperature in the atmosphere. Thus, several initiatives have been taken by the government to overcome the issue. This paper aims to provide a brief overview of the carbon emission trends and initiatives of the government toward global warming in Malaysia.