Carbon Emission Intensity Research Articles

Assessment and Driving Factors of Embodied Carbon Emissions in the Construction Sector: Evidence from 2005 to 2021 in Northeast China

Reducing embodied carbon emissions in the construction sector is pivotal for achieving sustainable development goals, mainly those related to health and well-being, sustainable cities and communities, and climate action. Hence, it is crucial to delve into the trends and influencing factors of construction-embodied carbon, especially in countries like China, where extensive construction projects are underway. Previous studies have investigated carbon emissions at both national and regional levels, whereas research specific to the construction sector in Northeast China remains limited. This study assessed the embodied carbon of the construction sector in Northeast China from 2005 to 2021. The results indicated that embodied carbon initially rose before declining, peaking at 278.9 MtCO2e in 2012. Based on the Logistic Mean Divided Index (LMDI) approach, the variations in embodied carbon were decomposed into seven driving factors, including emission source structure, embodied carbon intensity, construction mechanization, machinery requirement, industrial structure, economic development, and population scale. While construction mechanization and economic development were identified as primary drivers of the changes in embodied carbon, carbon emission intensity and population scale exerted inhibiting effects on the rise. Moreover, potential strategies for mitigating construction-embodied carbon in Northeast China were delineated, underscoring the regionality across different provinces. The results and suggestions can help foster a low-carbon construction industry from a provincial perspective.

Effect of Appointed Directors on Corporate Carbon Emission Intensity: Evidence from Mixed-Ownership Reform in Chinese Private Industrial Enterprises

The growing prominence of global warming has led to a worldwide consensus on the need to reduce carbon emissions. Employing a sample of private industrial enterprises listed on the Chinese stock market from 2008 to 2021, this study explores the effect of directors appointed by non-controlling state shareholders (appointed directors), which is a growing type of mixed-ownership reform, on corporate carbon emission intensity. The results show that appointed directors significantly reduce corporate carbon emission intensity. Mechanism tests suggest that this reduction is achieved through developing environmental strategies and increasing executive compensation incentives. Heterogeneity analyses reveal that the effect of appointed directors is more pronounced for firms with lax regional environmental regulation, in non-heavily polluting industries, with low analyst coverage, and with poor green innovation abilities. Our findings shed light on the effectiveness of mixed-ownership reform from the perspective of appointed directors and offer new implications and evidence for environmental protection and the sustainable development of enterprises in emerging markets.

Analysis of the carbon emissions trend in the Indian manufacturing sector: a decomposition and decoupling approach.

There is a growing emphasis on fostering green growth and lowering carbon emissions in order to achieve sustainable economic development. This study uses the Tapio decoupling model and analyzes the factors influencing changes in carbon emissions from manufacturing in India utilizing the log mean Divisia index (LMDI) techniques. Furthermore, the nexus between carbon emission intensity, information and communication technology (ICT), total factor productivity (TFP), skill, and energy intensity has been analyzed using the system-GMM approach. It is based on the plant-level Annual Survey of Industries (ASI) datasets for the organized manufacturing sector of India from 2001 to 2002 to 2019 2020 for the major 21 Indian states/UT. The findings reflect the presence of weak decoupling in the manufacturing sector both at the aggregate level and in states. This indicates that both output and emissions are increasing; however, output growth surpasses emission growth, which signifies an effort to transition towards more environmentally friendly production methods and enhanced energy efficiency. The output and population effect are found to be leading factors in carbon emissions, while energy intensity is found to be reducing the effect. Further, the system-GMM estimates show that ICT and energy intensity positively affect total factor productivity, while with an increase in carbon emission intensity, productivity declines. The study confirms the existence of an inverted N-shaped Kuznets curve in the sector. This present study will contribute to formulating energy and environmental strategies to reduce emissions and promote adopting cleaner energy sources. These efforts will facilitate the attainment of carbon neutrality and enhance energy efficiency within the sector.

Study on the Impact of China's Digital Economy on Agricultural Carbon Emissions

<p _msthash="770" _msttexthash="18511649104"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Under the background of China's “double carbon” goal, digital economy has become an important way to reduce carbon emissions in China. This paper utilizes the provincial panel data of China from 2012 to 2022, introduces the perspective of agricultural science and technology innovation, empirically examines the impact mechanism of regional digital economy development on agricultural carbon emission through regression analysis model, and portrays the dynamic effect and spillover effect of digital economy development on agricultural carbon emission from both time and space dimensions. The empirical results show that: digital economic development will have a significant inhibitory effect on the intensity of agricultural carbon emissions, and the inhibitory effect will be indirectly affected through the path of agricultural scientific and technological innovation; the impact of digital economic development on the intensity of agricultural carbon emissions there is a time lag effect, the current stage of the digital economic development will still have a strong inhibitory effect on the intensity of agricultural carbon emissions in the future; Digital economic development has a spatial spillover effect, i.e., the development of the regional digital economy will have an inhibitory effect on the intensity of agricultural carbon emissions in neighboring provinces. Based on this, it is proposed to strengthen the construction of digital infrastructure, promote the coordinated development of the digital economy in the region, and formulate policies to reduce carbon emissions in agriculture.</span></span></p>

Research on the impact of green logistics on carbon emission intensity of transportation industry

<p>With the serious challenge of global climate change, the Chinese government has proposed the "dual carbon" goals. In this context, the importance of green logistics as an effective way to reduce emissions in the transportation industry is becoming more and more prominent. This paper aims to explore the impact of green logistics on the carbon emission intensity of the transportation industry, in order to provide reference for relevant policy formulation and industry practice. Based on quantitative research methods, the development level of green logistics in 30 provinces in China is quantified using the entropy method, followed by an examination of the impact of green logistics on carbon emission intensity within the transportation industry through the construction of a benchmark regression model. The findings suggest that implementing green logistics can significantly reduce carbon emission intensity, with energy structure optimization being one channel for achieving this reduction. Additionally, improving information technology levels through green logistics can also help lower carbon emissions. Regional heterogeneity tests indicate that green logistics has a significant inhibitory effect on carbon intensity in western and northeast regions, while it is not as effective in central and eastern regions. Therefore, China should continue developing green logistics while considering regional differences and industry characteristics to achieve low-carbon development within the transportation industry.</p>

Towards Sustainable Development: The Impact of Agricultural Productive Services on China’s Low-Carbon Agricultural Transformation

In the context of carbon neutrality, the low-carbon transition in agriculture is crucial to achieving carbon mitigation through clean production. The provision of agricultural productive services (APS) is pivotal for modernizing farming practices in China. However, the impact of this on the low-carbon transformation has received limited attention. This research examined the non-linear relationship between agricultural productive services and low-carbon development, including verifying a threshold effect with APS as the threshold variable, employing panel data for 31 provinces in China from 2010 to 2021. The results of the study suggested that the effect of services associated with agricultural productivity on the transition of the agricultural sector to low-carbon practices varied across threshold ranges. Specifically, when the APS exceeded the threshold of 2.4396, a significant suppressive effect was observed on carbon emission intensity. Further analysis revealed that APS indirectly influenced the farmland scale and agricultural technological advancements, thereby promoting the low-carbon transition of China’s agriculture. Based on these results, it is recommended to intensify the development of APS in key cereal-producing regions, while emphasizing the harmonious progress of these services in conjunction with large-scale farmland management.

How does extreme heat affect carbon emission intensity? Evidence from county-level data in China

This study investigates the impact of increasingly frequent extreme heat events due to climate change on carbon emission intensities in China. Using spline regressions on county-level data from 2000 to 2019, we identify an asymmetric U-shaped relationship between daily mean temperatures and carbon intensities. Each additional day with temperatures above 33 °C in each location results in a 0.9% rise in the average annual carbon intensity, driven by higher energy consumption for cooling. We also explore the mitigating effects of China's low-carbon city initiatives and emissions trading scheme. By combining our estimation results with future temperature trajectories, we simulate that, in the long run, China's carbon intensity can increase by 8–13% under the SSP1-2.6 scenario and 24–51% under the SSP5-8.5 scenario. Our findings underscore the urgency for policymakers to thoroughly assess the socioeconomic impacts of heat waves and incorporate climate resilience into overall sustainable development plans.

Optimal capacity and multi-stable flexible operation strategy of green ammonia systems: Adapting to fluctuations in renewable energy

The potential of power-to-ammonia is increasingly recognized as a large-scale renewable electrical energy storage technology in the energy-transition landscape. Unlike conventional, continuously operating Haber-Bosch production processes, power-to-ammonia processes face operational challenges stemming from fluctuations in renewable power supply. Systematic strategies that can adapt to these fluctuations are required to improve the operational flexibility and stability of power-to-ammonia processes for industrial applications. This study proposes a multi-stable flexible ammonia operation strategy, which is practical and feasible in engineering. To maximize net profit, a design scheduling coordination optimization model is established and solved using a two-stage algorithmic framework of particle swarm and mixed integer linear programming. Additionally, three flexible scenarios are designed for the simulation analysis of a grid-connected green ammonia system. Compared with the traditional constant inflexible scenario, the multi-stable flexible scenario increases annual net profit by 5.05 M$ and reduces carbon emission intensity by 1.61 kg CO2/kgNH3. Furthermore, compared with the continuous flexible scenario, the volatility of the ammonia production load is reduced by 79.89 %. The multi-stable flexible operation strategy balances the green ammonia system’s economic, safety, and low-carbon attributes. Sensitivity analysis reveals that wind and photovoltaic complementary power generation reduces green ammonia costs and carbon emissions. When the selling price of ammonia exceeds 550 $/t, ammonia production and sales should be prioritized, but at lower ammonia prices, selling electricity to the grid to optimize economic efficiency is advised. This study provides novel insights into the flexible operation of the power-to-ammonia process and is expected to offer guidance for constructing and operating green ammonia systems.

Construction Land Transfer Scale and Carbon Emission Intensity: Empirical Evidence Based on County-Level Land Transactions in Jiangsu Province, China

The expansion of the construction land scale has been vital in supporting rapid economic development and meeting social needs. However, the spatial heterogeneity in the effect of construction land scale on carbon emission intensity at the county level remains underexplored. Therefore, comprehensively investigating the relation between the construction land transfer scale and carbon emission intensity holds substantial research value. Using panel data from 2007 to 2021, this study analyzes the spatiotemporal differentiation characteristics of carbon emission intensity and the effect of construction land scale on carbon emission intensity at the county level in Jiangsu Province, China. The findings reveal that carbon emission intensity at the county level in Jiangsu Province generally exhibits a continuous downward trend over time and a spatial distribution characterized by a gradual decrease from the southern counties to the central and northern counties. Moreover, there is a significant positive relation between the construction land transfer scale and carbon emission intensity, a conclusion supported by robustness tests. Furthermore, mediating analysis indicates that reduction of the construction land transfer scale exhibits a significant promoting effect on green technology innovation and industrial structure upgrading, which, in turn, has a significant inhibitory effect on carbon emission intensity. The impact of the construction land transfer scale from different sources, supply methods, types, and of county economic strength on carbon emission intensity has significant heterogeneity.

Will the Low-carbon City Pilot Policy Achieve Regional Emission Reduction?

In order to address climate change and promote green and low-carbon development, the Chinese government has implemented a pilot policy for low-carbon cities since 2010 and has continuously expanded the scope of the pilots. Taking the second batch of low-carbon pilot policies as an example, this paper uses difference in difference method to study the impact of low-carbon pilot policies on reducing urban carbon emission intensity based on the panel data of prefecture level cities from 2006 to 2019. It is found that the construction of low-carbon cities has a positive effect on reducing carbon emissions,and this conclusion is still valid under the robustness test. Based on this conclusion,this paper argues that China should further promote low-carbon pilot policies and actively explore the development model of low-carbon cities, especially to improve the livable and green urban environment.

Carbon Emission Reduction of Reclaimed Water Use Substitution for Inter-Basin Water Transfer and Sustainability of Urban Water Supply in Valley Area

Urbanization confronts the dual challenges of water scarcity and environmental degradation, prompting the exploration of diverse water sources for mitigating these impacts. Inter-basin water transfer (IBWT) has emerged as a solution to balance urban water demand and supply in areas with local water shortages. While IBWT can deliver high-quality water over long distances, it is costly, often contributing significantly to carbon emissions. Reclaimed water use (RWU) presents a promising alternative to address this dilemma. In this paper, a valley region of Chongqing municipality in Southwest China, which is confronted with water and environmental risks resulting from rapid urbanization, was explored and discussed as a case study to assess the potential impact of RWU on reducing carbon emissions as compared to IBWT. A method of accumulative accounting was adapted to calculate and sum up carbon emission intensities at various stages, revealing that the operational carbon emission intensities of IBWT and RWU are 0.7447 KgCO2/m3 and 0.1880 KgCO2/m3, respectively. This indicates that RWU substitution can reduce carbon emissions by 0.5567 KgCO2/m3 or 75%. This paper further elucidates the mechanism behind carbon emission reduction, highlighting the energy-saving benefits of using reclaimed water locally without recourse to extensive transportation or elevation changes. Additionally, this result presents three scenarios of reclaimed water use, including urban miscellaneous water, river flow replenishment, and agricultural irrigation in relation to their substitution effects and environmental impacts. Estimates of carbon emission reductions from reclaimed water use were projected at the planned scale, with the maximum potential of reclaimed water utilization predicted. Finally, this paper proposes an enhanced strategy to identify and prioritize factors affecting reclaimed water utilization and the effect of carbon emission reduction. This paper aims to facilitate the establishment of a robust legal, institutional, and managerial framework while fostering interdisciplinary and cross-sectoral cooperation mechanisms in valley urban areas. The methodology employed can be universally applied to other regions grappling with severe water stress, thereby facilitating endeavors toward carbon reduction and contributing significantly to the attainment of water sustainability.

Impact of green innovation on carbon reduction in China

Green innovation directly encompasses the two major concepts of green and innovation in the new development concepts, which provides a powerful driving force to support Chinese-style modernisation. This paper empirically tests the relationship between green innovation and carbon emission intensity using a double fixed effects model. Based on the panel data of 30 provinces in China, the mediation effect model of “green innovation-big data-carbon emission” is constructed. The result shows that green innovation has a noticeable direct negative effect on urban carbon emission intensity. The conclusions are robust after considering measurement errors and endogenous problems. Furthermore, it is found that big data plays a significant role in strengthening the relationship between green innovation and carbon emission intensity. The findings in this study not only advance the study on green innovation and carbon emissions but also provide a new perspective on the role of big data.

The Synergistic Effect of Urban Economic, Social and Space Factors on Residential Carbon Emissions: A Case Study on Provincial Capitals in China

Within the context of China’s commitment to carbon reduction goals, particularly in urban areas, addressing carbon emissions stemming from residents’ travel activities assumes paramount significance. Drawing upon established theoretical frameworks, this study advances several hypotheses delineating the determinants of low-carbon behaviors among urban residents. It analyzes panel data from 30 provincial capitals in China using a time–individual dual fixed effects model. This study empirically scrutinizes the posited theoretical model, aiming to elucidate the factors shaping urban residents’ low-carbon behavioral patterns and provide a decision-making basis for low-carbon construction and management of urban space. The findings underscore several notable associations. The disposable income, population density, and urban built-up areas exhibit significant positive correlations with carbon emissions among residents. Conversely, the urban gross domestic product (GDP) displays a significant negative correlation with carbon emissions. Furthermore, a positive correlation is discerned between the expanse of green spaces and the per capita carbon emissions intensity, while the availability of subway systems exhibits a negative correlation with both the per capita public green space area and the carbon emissions intensity. Notably, the configuration intensity of urban bus systems manifests an inverted U-shaped relationship with residents’ carbon emissions intensity. Specifically, within a certain threshold, an escalation in the bus equipment intensity coincides with heightened carbon emission intensity; however, beyond this threshold, a notable reduction in the per capita carbon emissions intensity ensues. Additionally, a U-shaped relationship is observed between the number of urban parks and residents’ carbon emissions intensity, indicating that an increase in parks may not necessarily contribute to carbon reduction efforts. Moreover, a discernible synergy is observed among various factors influencing carbon reduction efforts. These factors encompass residents’ education levels and disposable incomes, the presence of subway and regular public transportation systems, urban land utilization scales, economic development levels, green space provisions, public transportation infrastructure, population densities, and land equilibrium. This interplay underscores the interconnectedness and interdependence of diverse variables in shaping strategies for mitigating carbon emissions within urban contexts.

Carbon emissions forecasting based on temporal graph transformer-based attentional neural network

In the field of electric carbon, the mapping relationship between carbon emission flow calculation and power flow calculation was studied by combining techniques such as current trajectory tracking, carbon flow trajectory analysis, power system flow calculation methods, and electric network analysis theory. By delving into the mechanism between these two factors, a better understanding of the correlation between them can be achieved. In addition, by using time series data, graph attention neural networks (GNN), distributed computing technology, and spatiotemporal computing engines, carbon emission fluctuations can be decomposed and a high-frequency “energy-electricity-carbon” integrated dynamic emission factor can be achieved. Through the spatiotemporal distribution patterns of this dynamic factor in multiple dimensions, the carbon emissions from key industries in cities can be accurately calculated. In this paper, the LSTM-GAT model is used as the core to construct a key carbon emission prediction model for cities. The study focuses on the power plant, chemical industry, steel, transportation industry, and construction industry, which are high energy-consuming industries with an annual electricity consumption of more than 100 million kWh in a major city of China. By analyzing the entire life cycle from power generation to electricity consumption and conducting current flow analysis, monthly, weekly, and daily carbon emission calculations were performed. Additionally, other factors such as the industrial development index, GDP, coverage area of power generation enterprises, regional population, size, and type of power-consuming units were included in the comprehensive calculation to build a measurement system. By conducting experiments and analyzing historical data, we have found that the LSTM-GAT model outperforms the single models of GCN, GAT, LSTM, GRU, and RNN in terms of lower error values and higher accuracy. The LSTM-GAT model is better suited for predicting carbon emissions and related indicators with an accuracy rate of 89.5%. Our predictions show that the carbon emissions will exhibit a slow growth trend in the future, while the carbon emission intensity will decrease. This information can provide a scientific basis for government decision-making.

Decomposing Carbon Intensity Trends in China’s Civil Aviation: A Comprehensive Analysis from 1998 to 2019

Carbon emission intensity is an important index reflecting an entity’s low-carbon competitiveness. This paper presents an extended logarithmic mean divisia index (LMDI) model to dissect carbon intensity within China’s civil aviation from 1998 to 2019, revealing a significant reduction in CO2 emissions per air transport revenue. It attributes this decrease to technological advancements, optimized fleet structures, and improved operational efficiencies, highlighting the impact of larger, more efficient aircraft and enhanced load factors. The study also explores economic factors influencing carbon efficiency, suggesting a comprehensive approach encompassing technological innovation and strategic operational improvements for sustainable aviation development.

Can market-oriented and government-led spatial agglomeration of factories reduce carbon emission intensity? Evidence from China

Both market-oriented and government-led spatial agglomeration of factories profoundly affect China's carbon emission intensity. However, few studies have comprehensively examined the effect of spatial agglomeration of factories driven by different motivations on carbon emission intensity, and often ignore the moderating effect of local government attention to the environment. This study attempts to incorporate market-oriented and government-led spatial agglomeration of factories into the same analytical framework and examine their interaction effects on carbon emission intensity. We first calculate an Agglomeration Index based on the geographic coordinates of micro-factories to measure market-oriented agglomeration of factories, and take development zone policies as a proxy for government-led agglomeration of factories. Based on city-level data from 1998 to 2013, we empirically analyze their impacts on carbon emission intensity and further explore regional heterogeneity and mechanisms. The results show that both the market-oriented and government-led spatial agglomeration of factories can significantly reduce carbon emission intensity through scale effect, innovation effect and structure effect. However, the agglomeration of factories caused by government forces has weakened the original effect of market-oriented agglomeration of factories in reducing carbon emission intensity. Heterogeneity analysis indicates that government-led agglomeration of factories is more effective in reducing carbon emission intensity in less-developed regions. Furthermore, the improvement of the governments' attention to the environment can further exert the positive effect of spatial agglomeration of factories on carbon emission intensity. Our study deepens the understanding of the effects of spatial agglomeration of factories caused by different motivations, and provides a reference for China and other developing countries to formulate relevant policies for reducing carbon emission intensity.

Intelligence and carbon emissions: The impact of smart infrastructure on carbon emission intensity in cities of China

Urban infrastructures are facing environmental degradation induced by surging energy demand. There is an urgent need for countries to create a greener and more efficient infrastructure. Smart infrastructure (SI), as a new type of digitally enhanced infrastructure, has a strong potential to reduce carbon emissions. This study examines the nexus between smart infrastructure and carbon emission intensity (CI) in China, encompassing 251 cities from 2011 to 2019. The findings indicate that: (1) Every 1 % improvement in smart infrastructure reduces carbon emission intensity by 0.2810 %. This effect is significant only in more advantageous geographic locations and non-resource-dependent cities. (2) Panel threshold regression analysis demonstrates a nonlinear relationship between smart infrastructure and carbon intensity, showing a decreasing trend followed by an increasing trend under different energy consumption levels. (3) Mechanism analyses show that smart infrastructure affects carbon intensity through technological innovation and agglomeration effects. (4) Smart infrastructure exerts spatial spillover effects on carbon intensity with substantial spatial decay effects. This paper deepens the understanding of smart infrastructures and provides an empirical basis for the deployment of smart infrastructures to achieve carbon neutrality.

Do Short Sellers Target Firms with High Climate Risks? International Evidence

ABSTRACT Using an international sample of firms from 36 countries over the period 2002 to 2018, we investigate whether short sellers take firms’ climate risk into consideration when making investment decisions. Our empirical results show that short sellers increase their short-selling interest in firms with high carbon emission intensity but shun good carbon performers. Furthermore, the effect of climate risk on short-selling interest is more pronounced for firms operating in countries or regions that have adopted an emission trading scheme (ETS) as well as in countries characterized by higher regulatory quality and greater media pressure. We also document that short sellers in countries with stringent carbon regulations, higher environmental awareness, and superior environmental performance are more sensitive to climate risk. Our channel analyses highlight that stock overvaluation and an opaque information environment are two potential motives for short-selling interest in carbon-risky firms.

Advancing Sustainable Energy Development: A Study of the Factors Influencing Distributed Photovoltaic Industry in Heilongjiang Province

In today’s energy industry, the development of clean energy has become a global concern. As an important clean energy technology, photovoltaic power generation (PV) is widely used in various countries and regions, helping to reduce the dependence on traditional fossil energy sources and to reduce greenhouse gas emissions. Existing studies have some shortcomings in terms of factors affecting distributed PV grid-connected capacity. First, previous studies tend to focus on the influence of a single factor on the grid-connected capacity of distributed PV, ignoring the interaction effects among multiple factors. Second, the sample scope in existing studies is relatively narrow, often limited to specific regions or countries, and lacks comprehensive regional comparative analysis. In addition, previous studies often do not fully consider the moderating effect of carbon emission intensity on the influencing factors. Based on the above deficiencies, this study aims to build a regression model by analyzing panel data of central municipalities and provinces including Heilongjiang, Shandong and Beijing over the past three years, and construct an interaction model based on the interaction term. The empirical results show that effective sunshine hours, average feed-in tariff and annual electricity consumption are the main factors affecting the grid-connected capacity of distributed PV with a positive effect. The regional heterogeneity analysis also finds that carbon emission intensity enhances the impact of each factor on distributed PV grid-connected capacity. The regulation effect analysis confirms that the average feed-in tariff and annual electricity consumption have a regulation effect. With the results of this study, we fill the knowledge gaps left by previous studies and provide a new perspective to explore the influencing factors of distributed PV. A deeper understanding of the impact of these factors on distributed PV capacity helps us to better understand and optimize the potential of PV generation. In addition, our findings provide policy makers with recommendations and guidance on how to promote distributed PV generation, thereby contributing to the sustainable development of the clean energy sector.

Performance assessment of large-scale rooftop solar PV system: a case study in a Malaysian Public University

Adopting rooftop solar PV systems in various domestic and non-domestic sectors (including commercial, industrial, and agricultural) exhibits their commitment to green energy ventures. This study intends to evaluate the effectiveness of a grid-connected solar system that has been installed so far: a 6.9 MWp photovoltaic (PV) system implemented at University Tun Hussein Onn (UTHM) in Batu Pahat, Johor. This green energy system was installed as a part of the Self-Consumption (SELCO) program utilizing a Supply Agreement for Renewable Energy (SARE) contract. To assess the mounted energy system's efficiency, performance monitoring was carried out between December 2021 and November 2022. By evaluating the enactment of the 6.9 MWp solar system at UTHM, this present work has attempted to compile the achievement of implementation of self-consumption in terms of performance analysis and financial benefits to consumer. Based on International Electrotechnical Commission (IEC) 61724 standard, this study also investigates several performance characteristics of the PV system, such as final yield, clearness index (CI), array yield, PV module efficiency, inverter efficiency, reference yield, and capacity factor (CF) performance ratio (PR). Moreover, an assessment of Carbon Dioxide (CO2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2})$$\\end{document} avoidance was also conducted to quantify the amount of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} reduction achieved. In addition, the self-consumption ratio and self-sufficiency ratio pattern for solar power plant in UTHM has been assessed. The monthly clearness index at the project location from December 2021 to November 2022 ranges from 0.63 to 0.66. The PV system was monitored throughout this time and generated 8529 MWh of energy. The installed PV system has shown an efficiency of 11.86%, a final yield of 108.28%, and a PR of 0.78, respectively. Further, the installed 6.9 MWp PV system at UTHM will cut CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} emissions by 5450 tons annually. The 6.9 MWp solar PV system is also estimated to reduce 25.1 RM mil in 25 years. Making the system larger to 6.9 MWp would reduce the consumption from the grid during the week, makes it possible to achieve self-consumption levels of up to 100% and self-sufficiency of this solar plant is relatively low ranges from 22 to 35%. The research finding bridges the gap between policy makers, investors, and the public regarding acceptance of the large-scale Self-Consumption (SELCO) and strengthen the strategic collaborations between solar service provider and educational bodies to support of the government’s agenda to achieve carbon emission intensity reduction in Malaysia.Graphical