Mitigate Carbon Emissions Research Articles

Key influence factors of carbon emissions in Malaysian construction operations

The urgency to mitigate carbon emissions has gained significant societal attention as the consequences of climate change continue to unfold. There is a notable scarcity of studies conducted on carbon emissions for construction operations. Hence, the aim is to identify the key influence factor of carbon emissions to develop a carbon reduction framework for construction operations. A survey was conducted with 297 Malaysian construction stakeholders to analyze 18 factors. Descriptive and Rasch analysis were used. Results revealed the reliability and validity of the model, which could help to explore ways to reduce carbon emissions during the construction phase. The findings indicate that a gradual transformation process is necessary to expedite the transition towards a low-carbon construction. The research enhances the existing literature by emphasising the key factors for stakeholders who aim to efficiently manage carbon emissions during a construction project. There are different ideas on how to estimate and manage carbon emissions in construction projects, but there are no standardized ways. This study looks into the main factors that contribute to the generation of carbon emissions in construction, to identify ways to reduce them evaluating these factors from the perspective of different stakeholders.

Comprehensive assessment of waste heat recovery mismatch and renewable energy integration in data centers: A multifaceted energy, economic, and environmental perspectives

The integrated utilization of free cooling sources, waste heat recovery from information technology equipment, and renewable energy emerges as a pivotal method for conserving energy and mitigating carbon emissions in data centers. However, there is limited research on systems that integrate these three technologies. A notable mismatch arises between the waste heat generated and the requirements for building heating, but it is often overlooked. Moreover, a discernible lack of comprehensive analysis concerning the multifaceted influences associated with using renewable energy exists. Therefore, this paper employs a range of metrics to comprehensively assess a renewable energy integration system considering the mismatch of the waste heat recovery in data centers. The results indicate that due to mismatch characteristics, the energy-saving rate of heating systems will not constantly climb with the increase in energy savings. It causes only 45.5% of waste heat available for heating purposes achieving a power usage effectiveness of 1.17 and the rest will be abandoned. Similarly, its economic performance doesn't absolutely improve with the expansion in heating volume, the payback period doesn't continue to fall but increases after reaching the optimal level of 5.7. The proposed system accomplishes a 45.8% reduction in energy consumption for the cooling system, a notable decrease of 90% in carbon emissions, and a 55.7% reduction in annual cash flow. It is also important to note that while integrating battery systems aids in stabilizing grid disturbances, with a minimum disturbance index of 0.2, it introduces significant economic costs for the data centers.

Do Tradable Green Certificates Promote Regional Carbon Emissions Reduction for Sustainable Development? Evidence from China

The tradable green certificate (TGC) scheme is an important approach for mitigating carbon emissions within the context of a renewable energy development strategy and regional sustainable development. However, studies investigating the role of TGCs in encouraging carbon emissions reduction in China are limited and inconclusive due to ignoring the interference of other renewable energy policies and little distinguishing the impact of different green certificates. Using Chinese provincial data from 2013 to 2023, this study employs a difference-in-differences strategy to estimate the effect of the TGC policy on regional carbon emissions. The results reveal that the TGC policy significantly reduces provincial carbon emissions, and this reduction is predominantly contributed by certificate-electricity integration green certificates rather than certificate-electricity separation certificates. A 1% increase in the provincial trade volume of certificate-electricity integration green certificates can reduce total provincial carbon emissions by 0.8–1.3%. These findings hold across a series of rigorous robustness tests. This study also explains the different effects between certificate-electricity integration and certificate-electricity separation green certificates by the concept of additionality. To effectively reduce carbon emissions in the future, the TGC system must meet the requirement of additionality. These insights can provide reference for the improvement of TGC policy to better achieve the carbon reduction objective and sustainable development.

Digital Economy, Green Dual Innovation and Carbon Emissions

The digital economy serves as a pivotal catalyst for sustainable and eco-friendly development. This study employs a suite of advanced econometric models, including the fixed effects, mediation, threshold and moderation model, to elucidate the intricate dynamics by which the digital economy influences carbon emissions through the lens of green innovation. Building on the existing research on digital economy, green technology innovation and carbon emissions, this paper takes a dual-innovation perspective and divides green technology innovation into disruptive green technology innovation and incremental green technology innovation. And from the government and the public level, it explores how social concerns affect the effect of digital economy on carbon emissions. The analysis is grounded in a comprehensive dataset encompassing a decade of provincial-level data from 2011 to 2021 across China’s 30 provinces. The benchmark regression outcomes indicate the digital economy’s ability to substantially cut down carbon emissions; the threshold effect and mediating effect models demonstrate that a single-threshold effect exists and that disruptive and progressive green technological innovations mediate such ability. Additional research reveals that the digital economy’s impact on carbon emissions could be positively moderated by public and governmental attention. Eastern and western regions in China, as well as those with high levels of foreign investment and low levels of technological transaction activity, are more affected by the digital economy in terms of carbon emission reduction. Our conclusions offer practical recommendations for digital economy’s coordinated advancement and carbon emissions mitigation, and guide local governments to achieve sustainable development goals (SDGs).

Energy comparison and cost estimation of pressure-retarded osmosis using spiral wound membrane

Advancements in Pressure Retarded Osmosis (PRO) technology are enhancing the feasibility of evaluating its economic viability against other renewable energy production methods. This is done using the Levelized Cost of Energy (LCOE) as a metric. The study focuses on three PRO scenarios designed to minimize environmental impact and promote sustainable energy. These scenarios utilize a spiral-wound membrane module combined with hyper-saline solutions from Reverse Osmosis (RO) and wastewater from demineralization processes. Experimental results using a commercial spiral-wound membrane in the PRO system yielded LCOE values of USD 0.0702/kWh for a draw solution (DS) concentration of 36.2 g/l, USD 0.0563/kWh for 44.2 g/l, and USD 0.0721/kWh for 51.8 g/l. The study also evaluated environmental viability by considering the cost of CO2 emissions. This comprehensive comparison highlighted PRO's competitiveness with fossil fuels, showing it to be a reasonable alternative to coal and oil but less practical than natural gas. Specifically, the environmental analysis revealed that PRO is approximately 25.2 % more competitive than coal and 9.76 % more competitive than oil but 27.16 % less competitive compared to natural gas in terms of CO2 emission costs. This underscores the importance of considering carbon emission mitigation in energy generation.

Trilemma or Trinity? The nexus of economic growth, circular economy and net zero

How can economies achieve economic growth without causing negative environmental externalities? There are two aspects to the long-standing debate on ‘sustainable growth’. A first-best solution is for economies to replace fossil fuels with renewable energy sources, mitigating carbon emissions. A second-best solution is to also adopt efficient waste management, recycling residual waste and pollutants (including hard-to-abate carbon) from production (circular economy). We establish a simple growth model that integrates three fundamental pillars of economics: (i) the net zero carbon target in environmental economics (ii) the circular economy, dealing with waste management in resource economics, and (iii) sustainable growth, in growth economics. We argue that growth, circularity and net zero emissions present a trinity of solutions to the sustainable growth problem, showing that the circular economy is a necessary condition for achieving net zero. We show that an economy with ‘active’ environmental policy achieves net zero faster than one with ‘passive’ policy, and also eliminates carbon emissions.

Evaluation of Public Transportation System through Social Network Analysis Approach

In response to the phenomenon of global warming, the transportation sector aims to mitigate carbon emissions by promoting the use of public transportation. This study employs social network analysis to propose effective improvements to the public transportation system, focusing on bus stop locations and route networks in Hwaseong City, South Korea. Two networks were constructed based on existing public transportation routes and usage data at each bus stop. The findings and implications are as follows: Analyzing the public transportation network from a network perspective can effectively contribute to improving the public transportation network route system. By evaluating centrality and brokerage for the existing routes, it is possible to identify inefficient routes and develop efficient route modification plans. Based on actual usage patterns, excessive bus supply and unnecessary bus stop locations can be identified, allowing for the establishment of appropriate operational plans. This can lead to improved operational efficiency and cost savings. Rational route design and operational planning can enhance public transportation services and promote increased use of public transportation. Ultimately, this contributes to sustainable development through carbon reduction in the transportation sector.

China's progress in synergetic governance of climate change and multiple environmental issues.

Advancing the synergetic control of climate change and environmental crisis is crucial for achieving global sustainable development goals. This study evaluates synergetic governance levels over climate change and four environmental issues at the provincial level in China from 2009 to 2020. Our findings reveal significant progress in China's coordinated efforts to mitigate carbon emissions, reduce air pollutants, and conserve water resources. However, there remains room for improvement in managing solid waste and protecting ecological systems and overall progress in synergetic governance has slowed since 2015. Employing a random forest model, we identify socio-economic factors with great influence on synergetic climate change and environmental governance, such as energy intensity, service sector development, electronic equipment manufacturing, and transportation. Additionally, we reveal nonlinear relationships between some factors and performance of environmental subsystems, including both plateau effects (e.g. output in the smelting of ferrous metals) and U-shaped patterns (e.g. output in the manufacturing of metal products), possibly attributed to constraints in end-of-pipe treatment capacities and complexities in supply chain networks. Furthermore, through hierarchical clustering analysis, we classify provinces into four groups and provide tailored recommendations for policymakers to enhance synergetic governance levels in their respective regions. The framework established in this study also serves as a valuable reference for countries seeking to develop practical and context-specific solutions to mitigate climate and environmental risks.

Modeling cost-optimal fuel choices for truck, ship, and airplane fleets: The impact of sustainability commitments

For the next 25 years, numerous transport operators have introduced sustainability commitments to mitigate carbon emissions. However, static goals to reduce fossil fuel usage and adopt renewable fuel technologies pose economic risk. This study analyzes the costs of strategic fleet replacement through a mixed integer linear program. Comparing three Norwegian transport operators, we determine cost-optimal investments in fossil and renewable fuel technologies for two approaches: One integrating and the other neglecting sustainability commitments. Our findings reveal that the truck operator’s sustainability commitments incur minimal additional costs, with battery-electric trucks proving cost-effectiveness early. In contrast, ship and airplane operators exhibit significant differences in fleet replacement decisions, resulting in additional costs ranging from +6% to +31% for ships and +4% to +11% for airplanes, across fuel cost and carbon price scenarios. Norwegian carbon price regulations fall short in incentivizing a cost-competitive technology transition for ships and airplanes. Additional policies are needed to encourage gradual fleet replacement.

Strategic operation of electric vehicle in residential microgrid with vehicle-to-home features

The urgent need for innovative energy solutions to facilitate the transition to renewable energy and electric vehicles (EVs) is particularly critical in developing countries, where high emissions and grid reliability challenges persist. This study investigates an off-grid residential renewable energy system with stationary storage like battery and hydrogen, alongside an EV as mobile storage. A bi-level optimization framework is established considering stationary and mobile energy storage options and a strategy is proposed to implement Vehicle-to-Home (V2H) in off-gird settings. Three cases are studied, with optimal component sizing determined using an optimization tool integrated with MATLAB. Firstly, to determine the most cost-effective stationary storage alternative, Case 1 uses hydrogen and Case 2 employs a battery as storage. Then Case 3 integrates the optimal stationary storage with mobile storage using V2H technology enabling the EV to support household energy needs during peak times. Results highlight that combining stationary and mobile storage (Case 3) offers a 15.92% reduction in Total Net Present Cost (TNPC) and a 13.9% reduction in Levelized Cost of Energy (LCOE) compared with stationary storage alone. This off-grid system (Case 3) also outperforms the conventional grid economically and environmentally, offering 2.13× TNPC reduction and 7,813 kg/yr carbon emissions mitigation.

How do governance indicators, trade openness, industrialization, and population growth affect environmental degradation in BRICS Nations?

Against the backdrop of the dismal performance of BRICS nations in terms of governance, this study examines the relationship between governance indicators and carbon emissions. By employing DOLS and FMOLS cointegration techniques from 1996 to 2022, we find that effective governance helps in reducing environmental pollution. Specifically, indicators namely government effectiveness, political stability, the rule of law, voice and accountability, regulatory quality, and control of corruption show a favourable impact in mitigating carbon emissions. Additionally, our findings reveal that both foreign direct investments and trade openness contribute positively in lowering carbon emissions by introducing advanced technologies and sustainable practices. Conversely, rapid industrialization and population growth exacerbate carbon emissions due to increased reliance on fossil fuels and higher energy demand. Our study underscores the need for enhanced governance, green investments, and stringent environmental regulations to balance economic growth with environmental sustainability in BRICS nations.

Aqueous colloid flow batteries with nano Prussian blue

Flow battery is a safe and scalable energy storage technology in effectively utilizing clean power and mitigating carbon emissions from fossil fuel consumption. In the present work, we demonstrate an aqueous colloid flow battery (ACFB) with well-dispersed colloids based on nano-sized Prussian blue (PB) cubes, aiming at expanding the chosen area of various nano redox materials and lowering the cost of chemicals. Taking advantage of the two redox pairs of PB, the developed all-PB cell employing a low-cost dialysis membrane with the synthesized PB on both sides displays an open-circuit voltage (OCV) of 0.74 V. Moreover, when paired with an organic tetra pyridine macrocycle the cell with PB as positive electrolyte exhibits an OCV of 1.33 V and a capacity fade rate of 0.039 %/cycle (0.8 %/day). Redox-active colloids exhibit enduring physicochemical stability, with no evident structural or morphological changes after extensive cycling, highlighting their potential for cost-effective and reliable ACFB energy storage.

Towards carbon neutrality: The decoupling effect of industrial restructuring and non-fossil energy substitution on carbon emissions

China's carbon neutrality target requires early carbon decoupling of economic growth. Utilizing the input-output and programming technique, we investigate the carbon mitigation potential of industrial restructuring along with the substitution of non-fossil energy for electric production. Given the constraints of manufacturing stabilization and employment volatility magnitude, this paper finds that industrial restructuring can mitigate carbon emissions by 7.47 %. Besides, non-fossil energy substitution in electric production can strengthen the inhibiting effect, reducing carbon emissions from 7.47 % (currently about 31.5 % for non-fossil power generation) to 28.99 % (about 80 % in the future). This paper also simulates several scenarios for growth rates and estimates the prospective carbon reduction for the fourteenth Five-Year Plan in China. We indicate that the rising share of non-fossil energy is the linchpin to reducing energy-related carbon emissions, which can achieve absolute decoupling if the share of non-fossil energy in power generation reaches 67.1 % under different growth scenarios. This paper argues that carbon-based performance indicators, e.g., carbon intensity and carbon emissions, are more appropriate for local bureaucrats than energy-based ones. We provide valuable implications for the carbon decoupling path.

Adaptive Charging Simulation Model for Different Electric Vehicles and Mobility Patterns

Electric mobility is a sustainable alternative for mitigating carbon emissions by replacing the conventional fleet. However, the low availability of data from charging stations makes planning energy systems for the integration of electric vehicles (EVs) difficult. Given this, this work focuses on developing an adaptive computational tool for charging simulation, considering many EVs and mobility patterns. Technical specifications data from many EVs are considered for charging simulation, such as battery capacity, driving range, charging time, charging standard for each EV, and mobility patterns. Different simulations of charging many EVs and analyses of weekly charging load profiles are carried out, portraying the characteristics of the different load profiles and the challenges that system planners expect. The research results denote the importance of considering different manufacturers and models of EVs in the composition of the aggregate charging load profile and mobility patterns of the region. The developed model can be adapted to any system, expanded with new EVs, and scaled to many EVs, supporting different research areas.

No pain, no gain? Simulation of carbon reduction potential and socioeconomic effects of voluntary carbon trading in China during 2021–2060

Voluntary carbon market has become a key mechanism of stimulating carbon emission mitigation actions globally. In this study, we calculate the emission reduction potential of Carbon Generalized System of Preferences (CGSP), a dominated voluntary carbon trading mechanism in China, including on-going projects of forestry carbon sinks, waste clothes reuse, installation of distributed photovoltaic power generation system, and usage of energy-conserving devices, as well as other extended methods for their future mitigation potentials. We employ a computable general equilibrium model to delve into the effects of CGSP mechanism on carbon price, economic growth and household consumption, assessing its impact on the equitability of social welfares between urban and rural residents during 2021–2060. Our results indicate that employing the current methods for CGSP would lead to around 5.0 Mt CO2 reduction annually. since 2018, whereas adopting the extended methods would yield reductions of 157.1 Mt. Promoting forestry carbon sinks and energy-conserving appliances offers the most prominent emission reduction, while low-carbon travel shows the biggest potential going forward. CGSP will loosen carbon constraints on industries in the market and result in a lower carbon price, reducing the economic cost of carbon trading without compromising China's carbon peaking and carbon neutrality goals. Supplementing CGSP could reduce the GDP loss by up to 0.2 trillion Yuan by 2030 and 0.9 trillion Yuan by 2060, compared to the sole operation of compliance carbon trading market. Provided fiscal stimulus policies, CGSP will further reduce GDP loss by 1.1 trillion Yuan in 2060. Different fiscal stimulus scenarios have a strong implication on welfares of urban and rural residents, wherein monetary equivalent incentive will reduce urban-rural income gap by up to 718 Yuan/capita in 2060.

Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China

Artificial intelligence (AI) has become a key driver in the latest wave of scientific and technological advancement, and its rapid development, proliferation, and environmental impacts cannot be ignored. China and numerous emerging economies are confronted with the dual challenges of environmental degradation and climate change. Hence, it is imperative to assess whether the advancement of AI can contribute to a synergistic reduction in pollutant and CO2 emissions. This paper utilizes the system-generalized method of moments (SYS-GMM) to study the synergistic effect of artificial intelligence on mitigating pollutant and carbon emissions. The following three main conclusions are drawn: (1) AI plays a major role in synergistically decreasing pollutant and CO2 emissions; (2) AI indirectly helps lower pollutant and CO2 emissions by fostering technological advancements and enhancing industrial structures. Although it contributes to an increase in emissions by expanding production scale, its suppression effect dominates overall; (3) The impact of AI applications is particularly vital in cities with strict environmental controls, especially in the central and eastern regions. Finally, we suggest some policy measures to augment the influence of AI in reducing emissions and attaining sustainable development.

Do sustainable supply chain practices mitigate carbon emissions? The role of supplier environmental, social and governance training

Do sustainable supply chain practices mitigate carbon emissions? The role of supplier environmental, social and governance training

Pre-training enhanced spatio-temporal graph neural network for predicting influent water quality and flow rate of wastewater treatment plant: Improvement of forecast accuracy and analysis of related factors

Efficient management of wastewater treatment plants (WWTPs) necessitates accurate forecasting of influent water quality parameters (WQPs) and flow rate (Q) to reduce energy consumption and mitigate carbon emissions. The time series of WQPs and Q are highly non-linear and influenced by various factors such as temperature (T) and precipitation (Precip). Conventional models often struggle to account for long-term temporal patterns and overlook the complex interactions of parameters within the data, leading to inaccuracies in detecting WQPs and Q. This work introduced the Pre-training enhanced Spatio-Temporal Graph Neural Network (PT-STGNN), a novel methodology for accurately forecasting of influent COD, ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), pH and Q in WWTPs. PT-STGNN utilizes influent data of the WWTP, air quality data and meteorological data from the service area as inputs to enhance prediction accuracy. The model employs unsupervised Transformer blocks for pre-training, with efficient masking strategies to effectively capture long-term historical patterns and contextual information, thereby significantly boosting forecasting accuracy. Furthermore, PT-STGNN integrates a unique graph structure learning mechanism to identify dependencies between parameters, further improving the model's forecasting accuracy and interpretability. Compared with the state-of-the-art models, PT-STGNN demonstrated superior predictive performance, particularly for a longer-term prediction (i.e., 12 h), with MAE, RMSE and MAPE at 12-h prediction horizon of 2.737 ± 0.040, 4.209 ± 0.060 and 13.648 ± 0.151 %, respectively, for the algebraic mean of each parameter. From the results of graph structure learning, it is observed that there are strong dependencies between NH3-N and TN, TP and Q, as well as Precip, etc. This study innovatively applies STGNN, not only offering a novel approach for predicting influent WQPs and Q in WWTPs, but also advances our understanding of the interrelationships among various parameters, significantly enhancing the model's interpretability.

Tweaking the structure and symmetry of Y2B2O7:Eu3+ by B-site engineering for efficient and thermally stable phosphor: Y2Zr2O7 versus Y2Ge2O7

There is an urgent need for synthesizing red emitting phosphor with high photoluminescence quantum yield (PLQY) and good thermal stability for high performing phosphor converted light emitting diodes (pc-LEDs) which are in demand to mitigate carbon emission. Here we have achieved the same through symmetry alteration and structural modification strategy which leads to high PLQY and improved thermal stability. In this work we have synthesized highly symmetric cubic fluorite Y2Zr2O7:Eu3+pyrochlore and replacing the B-site Zr4+by Ge4+ to lower the symmetry and induce structural change to tetragonal Y2Ge2O7:Eu3+ by organic solvent free solid state reactions. Among the three composition Y2Ge2O7: Eu is exhibiting higher emission intensity, higher asymmetry ratio and enhanced PLQY (32.3 %) by virtue of lower symmetry of tetragonal phase. On the other hand, higher thermal stability was achieved for Y2Zr2O7: Eu (97 % at 450 K) endowed by higher structural integrity and stability of pyrochlore phase.

Modeling carbon dioxide emissions reduction

The paper proposes a new scientific approach to modeling carbon dioxide (CO2) emissions in individual countries, based on the tools of Kohonen self-organizing maps and the provisions of economic theory. This work is based on conducted a comprehensive analysis of wide set of indicators influencing carbon dioxide emissions and decarbonization processes in 40 selected countries for 10 years time span from 2013 till 2022. During clustering these nations using self-organizing maps, 14 key indicators were chosen that cover economic and demographic growth, energy consumption, CO2 emission data, and includes trade of energy recourses for wider analysis and identification of countries with similar decarbonization potential, economic development and trade energy recourses possibilities. This approach exposed distinct clusters of nations with varying decarbonization capabilities and tracked the progress of analyzed countries in terms of CO2 emission changes in dynamic.The analysis of clustering was focused on identification leaders and followers in decarbonization activities. The leaders cluster includes Sweden, Norway, New Zealand and other countries exhibited leadership in developing a climate-resilient economy among 40 countries. Hence, all countries positioned closer to the leader cluster on the map demonstrated higher efficiency in their decarbonization pathways. Consequently, these properties of Kohonen maps provide a foundation for formulating general recommendations to achieve an efficient and effective low-carbon economy for followers including Ukraine, Kazakhstan and other energy intensive countries.Distinct countries decarbonization profiles resulting from clustering were used to develop practical recommendations towards estimating and establishing targeted CO2 emission levels for analyzed countries, conducting continued promoting renewable energy utilization, enhancing cross-border energy trade, diversifying energy portfolios, etc. Also, the proposed methodology can be utilized to project future emissions trends for each country based on cluster-specific models and facilitate policy decisions making process related to mitigating carbon emissions.