Emission Reduction Strategies Research Articles

An Interpretable Time Series Forecasting Model for Predicting NOx Emission Concentration in Ferroalloy Electric Arc Furnace Plants

Considering the pivotal role of ferroalloys in the steel industry and the escalating global emphasis on sustainability (e.g., zero emissions and carbon neutrality), the demand for ferroalloys is anticipated to increase. However, the electric arc furnace (EAF) of ferroalloy plants generates substantial amounts of nitrogen oxides (NOx) because of the high-temperature combustion processes. Despite the substantial contributions of many studies on NOx prediction from various industrial facilities, there is a lack of studies considering the environmental condition of the EAF in ferroalloy plants. Therefore, this study presents a deep learning model for predicting NOx emissions from ferroalloy plants and further can provide guidelines for predicting NOx in industrial sites equipped with electric furnaces. In this study, we collected various historical data from the manufacturing execution system of electric furnaces and exhaust gas systems to develop a prediction model. Additionally, an interpretable artificial intelligence method was employed to track the effects of each variable on the NOx emissions. The proposed prediction model can provide decision support to reduce NOx emissions. Furthermore, the interpretation of the model contributes to a better understanding of the factors influencing NOx emissions and the development of effective strategies for emission reduction in ferroalloys EAF plants.

Case study on carbon emission of a traditional Chinese medicine pharmaceutical enterprise toward carbon neutrality

Against the backdrop of carbon peaking and carbon neutrality, traditional Chinese medicine pharmaceutical enterprises (TCMPE) with high energy consumption are facing a significant challenge in reducing carbon emissions. It is of great significance to conduct detailed carbon emission accounting and develop corresponding emission reduction strategies for TCMPE. This study investigated the calculation of carbon emissions from TCMPE, with a case study focusing on the Shangyao Xingling (SYXL) Qingpu Pharmaceutical Base in Shanghai, China. Furthermore, the accounting of carbon emissions within its operational boundaries was conducted. The results indicated that the carbon emissions of the SYXL primarily stemmed from indirect emissions resulting from purchased electricity and heat, accounting for 80.7%. Meanwhile, sewage treatment also generated significant carbon emissions. Gray carbon, blue carbon, and red carbon were used to represent various types of carbon emissions, corresponding to different emission reduction concepts. Energy conservation, efficiency improvement, and the development of renewable energy will be crucial for TCMPE to achieve carbon neutrality. At the same time, some carbon offsets will also be required. Furthermore, the existing issues with the current accounting system were discussed, and pertinent suggestions were proposed.

Atmospheric microplastic deposition associated with GDP and population growth: Insights from megacities in northern China

Microplastic (MP) pollution is evolving into one of the most pressing environmental concerns worldwide. This study assessed the impact of economic activities on atmospheric MP pollution across 17 megacities in northern China, analyzing the correlation between the deposition flux of atmospheric MPs and variables such as city population, gross domestic product (GDP), and industrial structure. The results have shown that the MP pollution is obviously impacted by human activities related to increased GDP, population, as well as tertiary service sector, in which the MP pollution shows most close relationship with the GDP growth. Polypropylene, polyamide, polyurethane, and polyethylene were identified as the primary components of atmospheric MPs. The average particle size of MPs in atmospheric dustfall is 78.3 µm, and the frequency of MP particles increases as the particle size decreases. The findings highlight the complex relationship between socio-economic development and atmospheric MP accumulation, providing essential insights for the formulation of targeted emission reduction strategies.

Reducing emissions from production and distribution in three-echelon supply chains

Reducing carbon emissions is one of the key goals of contemporary supply chains and logistics. This paper aims to formulate a novel three-echelon (supplier-3PL-retailer) supply chain model comprising green technology investment, variable demand, variable lead time, a dynamic shipping strategy, and an option for vehicle selection. The demand function considers the product's freshness index and maximum lifetime, along with the availability of stocks. Besides, the delivery from the 3PL to the retailer is dynamically determined based on previous shipment information. A green vehicle is selected using a multi-criteria decision-making (MCDM) technique based on q-alternatives with n-characteristics for the transportation process and mathematically we link this selection process to traditional three echelon supply chain optimization problem. The results show that combining green investment with an appropriate dynamic shipping strategy by using a selected green vehicle reduces the supply chain's emissions and total integrated cost. Furthermore, reducing lead time without compromising other supply chain factors stimulates demand of customers and produces fewer emissions with a significant reduction in the total integrated cost. A real-case-based study shows the performance of the proposed model and method.. The study illustrates that 47.744% of the carbon emissions in Scenario 1 and 47.53% in Scenario 2 are reduced when an emission reduction strategy is implemented. In terms of total integrated costs our proposed model could save 4.85% cost in Scenario 1 while 4.96% for Scenario 2, which is significant.

Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method

Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the “Air Pollution Prevention and Control Action Plan” in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS–Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T–mode principal component analysis (T–PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 μg m−3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 μg m−3 and 0.96 μg m−3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %–74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %–49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.

Stochastic differential game of joint emission reduction in the supply chain based on CSR and carbon cap-and-trade mechanism

This paper considers both carbon emission reduction and corporate social responsibility (CSR) goodwill as endogenous variables in the joint emission reduction process of the supply chain system. The joint emission reduction strategy of the three-tier supply chain system is examined from a long-term perspective utilizing stochastic differential game models. Three models, namely the cooperative game, the Nash non-cooperative game, and the Stackelberg master–slave game, are developed to analyse and compare the equilibrium strategies under each model. The results indicate that the manufacturer-led Stackelberg master–slave game can achieve the Pareto improvement in the profits of the supplier, manufacturer, retailer, and the entire supply chain system.

AI-Enhanced Comprehensive Analysis Of The Research Perspective On The Microbiome And Methane Production In Cow Rumen

Methane emissions from ruminant livestock, particularly cows, have been a topic of interest because of their negative environmental impact, including contributing to greenhouse gas accumulation and accelerating climate change. Understanding the cow's rumen microbiome can provide valuable insights for devising effective emission reduction strategies. Despite the recognized environmental impact of methane emissions from cows, however, a systematic review on the cow rumen microbiome has not yet been done. This study aims to fill this gap by performing an unbiased bibliometric analysis to review the effect of the rumen microbiome on methane emissions through cross-check of multiple data sources. While the bibliometric analysis offers a quantitative approach to assess research trends and identify areas of potential exploration within the field of the cow rumen microbiome, this study also evaluated the concurrently growing interest in the potential of artificial intelligence (AI) tools, specifically ChatGPT by OpenAI in terms of its efficacy and accuracy in enhancing scientific research project. Through the fusion of conventional research methodologies and AI-driven insights, this study aspires to provide a holistic perspective on the cow rumen microbiome's profound influence on methane emissions, while also exploring the untapped potential of AI in advancing scientific inquiry.

Inequality of carbon emissions between urban and rural residents in China and emission reduction strategies: evidence from Shandong Province

China is actively heeding the call and striving for a low-carbon and environmentally friendly development route as part of the general trend toward a global low-carbon economy. The rapid economic development of our nation has brought to light the issue of carbon emissions resulting from the consumption habits of residents. This paper delves into the topic by conducting a thorough analysis of the carbon emission of residents’ consumption using the input-output method. Based on the survey findings, the disparity in carbon emission between urban and rural communities in Shandong Province is evident not only in terms of total emissions but also in different categories and regions. Urban residents generate a total carbon emission of approximately 70.2921 million tons, which is three times higher than that of rural residents at 23.7846 million tons. The carbon emission of both urban and rural residents is primarily attributed to their embodied carbon emission. In examining the composition of the implied carbon emission of residents’ consumption in Shandong Province, urban residents’ carbon emission is concentrated in three areas, namely, food, culture, education, entertainment, transportation, and communication, accounting for 21.45%, 20.88%, and 15.10%, respectively. Conversely, rural residents’ carbon emission is concentrated in four areas, including clothing, food, culture, education, entertainment, transportation, and communication, accounting for 29.01%, 17.45%, 15.43%, and 13.36%, respectively. Finally, according to the characteristics of the consumption carbon emission of urban and rural residents in Shandong Province, we give corresponding emission reduction strategies.

Enhancing micro grid sustainability: Genetic-driven reptile search algorithm based carbon emission and cost reduction strategy

Grid architecture is continuously changing in response to modern energy rules that ensure the integration of more renewable sources to lower the carbon footprint. The integration of non-dispatchable renewable energy sources (RESs) using low voltage grids is a possible alternative to a centralized method. Due to a recent trend, microgrids (MGs) are reducing their reliance on the primary grid by incorporating localized sources of electricity including energy storage devices, micro turbines, and fuel cells. Due to the expanding number of spread generating sources with diverse features, power dispatching schedule for distribution micro grids is becoming more challenging. This study offers a dispatch scheduling method from the perspective of an operator. The primary objective of this study is to employ the Genetic-driven Reptile search algorithm (G-RSA) for the dual purpose of minimizing carbon emissions and reducing costs within each microgrid. Additionally, fewer sale and purchase are made from the primary grid. Transmission losses are consequently reduced.

Substance-flow analysis and emission-reduction strategies for thallium in the steel industry process

Thallium, which is a highly toxic and sparsely distributed element, is often found in various mineral compounds. Its release into the environment primarily occurs through the mining of metals, metallurgical processes, and the combustion of fossil fuels. These activities release thallium from minerals, concentrating it in ash, wastewater, and exhaust gases, thus contributing to thallium pollution. With over 600 active steelworks in China continuously producing steel, the risk of thallium pollution is significant and thus presents a major challenge. This research focuses on understanding the mechanisms of the physical phase evolution, migration pathways, and emission characteristics of thallium in steelworks. For the first time, a thallium flow network in steelworks is developed using substance flow analysis. This involves examining the input, output, waste discharge characteristics, and potential for reduction of thallium. The findings reveal that iron concentrate is the primary source of thallium in steelworks, with dust being the main form of thallium output. The quantities of thallium input, output, and circulation are found to be 425.44, 365.49, and 49.66 mg/t-CS, respectively. Implementing source control, endpoint governance, or a combination of both can reduce pollutant emissions in steelworks by 41.47%, 43.35%, and 65.63%, respectively. This study enhances our understanding of thallium pollution in steelworks.

Neutronic and Proliferation Resistance Analysis of Small Modular Pressurized Water Reactor with Various Fuel Types using SRAC 2006

One strategy for emission reduction in Indonesia is the dedieselization program, replacing diesel power plants with renewables. Pressurized Water Reactor (PWR) type Small Modular Reactor (SMR) is also a consideration. This study aims to compare fuel performance in SMRs. Four fuel types with varying fissile and fertile nuclides but equal fissile mass percentages were assessed: U235- U238, U235-Th232, U233-U238, and U233-Th232, using PIJ SRAC 2006 and JENDL 4.0. Parameters include neutron multiplication factor, conversion ratio, and isotope production. Results indicated that U233-U238 has a highest initial neutron multiplication factor, 1.55. Substituting uranium-238 with thorium-232 boosts neutron flux and conversion, reducing nuclear proliferation risk. While initial neutron multiplication may not be highest, thorium-232 can produce uranium-233 with high initial neutron multiplication factor. This research aids fuel selection for Indonesia SMR dedieselization program.

Land use data can improve the accuracy of carbon emission spatial inversion model

AbstractInvestigating the spatial distribution and correlation characteristics of carbon emissions would be conducive to the policy formulation for precise carbon emission spatial reduction. Firstly, a new carbon emission spatial inversion model was developed, incorporating nighttime light data and land use data. After verifying the validity and accuracy of the inversion results, the continuous carbon emission spatial data in the Beijing‐Tianjin‐Hebei Urban Agglomeration (BTHUA) were acquired from 2000 to 2019. Then, the spatial distribution and correlation characteristics were further analyzed in the BTHUA. Finally, policy recommendations were proposed for carbon emission reduction and urban sustainable development. The results showed that the built model can improve the accuracy of the carbon emission spatial inversion data. The carbon emissions were low in the northwest and high in the southeast of the BTHUA, with a noticeable expansion of the high carbon emission contiguous areas around Beijing, Tianjin, Shijiazhuang, and other prefecture‐level cities, which was consistent with the socioeconomic development pattern. The center of gravity of carbon emissions moved to the southeast, showing a relatively stable distribution. The spatial correlation degree of carbon emissions among cities gradually increased, with Beijing and Tianjin playing a prominent role. As a scientific tool, the spatial inversion model helps to produce more accurate spatial data. The results and conclusions can provide useful and scientific references for spatial analysis and regulation strategies of regional carbon emission reduction.

Strategic pathways to sustainable energy: Carbon emission pinch analysis for Bangladesh's electricity sector

Balancing burgeoning energy demands in the electricity sector while curbing carbon emissions poses a formidable challenge for emerging economies like Bangladesh, which is heavily reliant on fossil fuels. Despite the country's power system master plan (PSMP) until 2041 and submitted intended nationally determined contributions (INDC) to tackle mounting energy needs and associated emissions, the PSMP lacks specific emission reduction strategies. Thus, this study employs carbon emission pinch analysis to facilitate Bangladesh's long-term energy planning, highlighting emission reduction hurdles. This research aims to set emission limits, delineate fossil fuel and carbon-neutral source (i.e., zero emission during operation) compositions, ascertain carbon-neutral source ranges for targeted emissions, and propose viable carbon-neutral sources to meet escalating energy demands. Three scenarios are extensively explored: fulfilling INDC mandates, a 10% increase in renewable energy, and maintaining 2035 emission levels in 2040. The analysis unveils emission limits of 55 758.83 and 84 778.61 × 109 grams of CO2 equivalent for 2025 and 2030, respectively. Projections indicate a foreseen 10% surge in renewable energy by 2035, elevating its share to 18.16%. Carbon-neutral energy sources, encompassing solar, wind, hydroelectricity, biomass, and nuclear, are estimated to cover 56.06% of energy demand by 2040, driving a 33.30% emissions reduction.

SCOPE 3 CARBON EMISSIONS IN SEAPORTS

AbstractGrowing concern about carbon emissions and their environmental impact has led the port sector to assess and reduce their carbon footprint. Scope 3 mobile source emissions, especially related to maritime transportation, play a significant role in this scenario. This study analyses methodologies and tools for calculating Scope 3 emissions, highlighting the main features and shortcomings through case studies. Among the main identified gaps are the lack of standardization and the limited availability of port data. Digitization and its integration with a universal approach present an opportunity for more accurate calculations and a more effective development of emission reduction strategies.

A study on the dynamic impact of carbon emission trading on green and high-quality development.

As a pivotal element in market mechanisms, carbon trading is increasingly recognized as crucial for achieving China's Carbon Peaking and Carbon Neutrality Goals. This study introduces a comprehensive dynamic model, integrating carbon trading, emissions, economic growth, and green technology innovation, to offer a holistic understanding of the interplay between these domains. Utilizing principles from nonlinear dynamics and chaos theory, the model is adept at simulating various scenarios and assessing the effectiveness of government policies in stabilizing these complex systems. In-depth analysis provided by this research sheds light on the nuanced impact of carbon trading policies on sustainable development. Key findings highlight (1) Carbon trading's essential role as a catalyst in propelling sustainable and high-quality growth. (2) A strong positive relationship is observed between the sophistication of the carbon trading mechanism and its effectiveness in stimulating green technology innovation and fostering high-quality green development. Notably, carbon trading's influence on green technology innovation markedly enhances the efficacy of carbon emission reduction strategies. (3) Government regulations are instrumental in augmenting carbon prices, thus incentivizing increased corporate participation in emission reduction and enhancing the overall impact of carbon emission reduction. Nevertheless, the study identifies a critical threshold in regulatory intensity, beyond which there is a risk of system destabilization ( ). These findings underscore the imperative for developing an integrated national carbon emission trading market, prioritizing sustainable growth strategies and diligently pursuing China's environmental objectives.

Efficiency analysis and CO2 emission reduction strategies in the US forest sector: a data envelopment analysis approach

Industrial economic activities produce pollutants and environmentally sustainable production systems in forestry aim to minimize these undesirable outputs while maintaining high production and economic growth. In this contribution, we assume that in addition to plot-specific inputs and outputs, there are some contextual variables that may be exogenously fixed or may be under the control of the decision-makers. In this sense, we first propose a novel and practical approach to calculate environmental efficiency by reducing undesirable products. Then, we utilize an inverse data envelopment analysis (IDEA) model to effectively manage and reduce CO2 emissions. In doing so, the applied models have been utilized to evaluate the efficiencies of 89 forest plots in the USA. Given our estimations in a real application to the forest plots, the study revealed that the average environmental efficiency score is nearly 0.75 (out of 1). However, there is potential for improvement by adjusting the impacts of contextual factors, which could raise the score to approximately 0.8. Furthermore, the analysis indicates a positive correlation between ownership and environmental efficiency, suggesting that increased ownership leads to higher environmental efficiency. Conversely, temperature exhibits a negative correlation with environmental efficiency. Finally, the results obtained from the IDEA indicate that in order to reduce undesirable outputs by a specific level of 5–10%, it is necessary to decrease other inputs and outputs. This is because, under the assumption of weak disposability, reducing the level of undesirable outputs requires a reduction in certain factors that influence production capacity. In other words, achieving the desired reduction in undesirable outputs inevitably involves diminishing certain aspects of the production process. As the major conclusion, the emergence of IDEA as a powerful tool for sensitivity analysis, along with its flexible nature, offers exciting opportunities for research and practical applications in various fields, including forestry activities. It has the potential to enhance overall environmental efficiency and enable better control over GHG emissions levels.

Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model.

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

THE ROLE OF ENVIRONMENTAL HEALTH AND SAFETY PRACTICES IN THE AUTOMOTIVE MANUFACTURING INDUSTRY

The automotive manufacturing industry plays a pivotal role in global economic development, providing transportation solutions while simultaneously facing multifaceted challenges related to environmental health and safety (EHS) practices. This review investigates the indispensable role of EHS practices within the automotive manufacturing sector, highlighting their significance in mitigating environmental impact, ensuring workplace safety, and complying with regulatory standards. Effective EHS practices are integral to managing environmental sustainability within automotive manufacturing. These practices encompass waste management, emissions reduction, and resource conservation strategies aimed at minimizing the industry's ecological footprint. Additionally, the adoption of eco-friendly technologies and processes, such as renewable energy integration and material recycling, contributes to the industry's overall environmental stewardship. Furthermore, prioritizing workplace safety is imperative in the automotive manufacturing sector due to its inherently hazardous operational environments. EHS initiatives focus on risk assessment, hazard identification, and the implementation of preventive measures to safeguard employees from occupational injuries and illnesses. Moreover, fostering a culture of safety awareness through training programs and regular audits promotes a conducive working environment conducive to employee well-being and productivity. Compliance with regulatory standards is a cornerstone of EHS management in the automotive manufacturing industry. Adherence to local, national, and international regulations ensures operational legality and enhances corporate reputation. Through continuous monitoring and assessment, automotive manufacturers strive to stay abreast of evolving regulatory frameworks, thereby aligning their practices with industry best practices and societal expectations. The integration of robust EHS practices is indispensable for sustainable operations and corporate responsibility within the automotive manufacturing industry. By addressing environmental concerns, ensuring workplace safety, and meeting regulatory requirements, automotive manufacturers can uphold their commitment to environmental stewardship and social accountability while maintaining operational efficiency and competitiveness in the global market.
 Keywords: Environment, Health, Safety, Automotive, Manufacturing, Industry.

Vertical measurements of atmospheric CO2 and 14CO2 at the northern foot of the Qinling Mountains in China

The CO2 and 14CO2 levels in air samples from the northern foot of the Qinling Mountains (Xi'an, China) were determined. In 2021, a hexacopter unmanned aerial vehicle sampled air at different heights, from near-ground to 2000 m. The objectives of this study were to determine vertical characteristics of CO2 and 14CO2, the sources of different-height CO2, and the influence of air mass transport. The CO2 concentrations mainly exhibited a slight decreasing trend with increasing height during summer observations, which was in contrast to the increasing trend that was followed by a subsequent gradual decreasing trend during early winter observations, with peak CO2 levels (443.4 ± 0.4–475.7 ± 0.5 ppm) at 100–500 m. The variation in vertical concentrations from 20 to 1000 m in early winter observations (21.6 ± 19.3 ppm) was greater than that in summer observations (14.6 ± 14.3 ppm), and the maximum vertical variation from 20 to ∼2000 m reached 61.1 ppm. Combining Δ14C and δ13C vertical measurements, the results showed that fossil fuel CO2 (CO2ff, 56.1 ± 15.2 %), which mainly come from coal combustion (81.2 ± 3.4 %), was the main contributor to CO2 levels in excess of the background level (CO2ex) during early winter observations. In contrast, biological CO2 (CO2bio) dominated CO2ex in summer observations. The vertical distributions of CO2ff in early winter observations and CO2bio in summer observations were consistent with those of CO2 during early winter and summer observations, respectively. The strong correlation between winter CO2bio and ΔCO (r = 0.81, p < 0.01) indicated that biomass burning was the main contributor to CO2bio during early winter observations. Approximately half of the air masses originated from the Guanzhong Basin during observations. The results provide insights into the vertical distribution of different-sources of atmospheric CO2 in scientific support of formulating carbon emission-reduction strategies.

Investigating Barriers to Low-Carbon Policy Implementation among Mining Companies in Ghana

Efforts for carbon emission reduction have been identified as a major climate change mitigation target in the Paris Agreement. The 26th Conference of Parties to the United Nations Framework Convention on Climate Change highlighted the worldwide effort for cleaner and greener mining, urging the mineral industry to embrace greener operational emissions reduction technologies and strategies. At the Paris Conference, Ghana pledged to reduce CO2 emissions by 15% and increase its renewable energy share to 10% of the national energy mix. Ghana’s efforts to implement a low-carbon policy were, however, hampered for several reasons. Ghana is Africa’s leading and the world’s seventh largest gold producer. Mining has contributed significantly to foreign currency acquisition and government revenue. The gold mining sector contributes approximately 95% of Ghana’s total mineral revenue. Considering these situations, the fundamental question is how Ghana’s mining industry can contribute to CO2 emission reduction efforts. This paper investigates barriers to low-carbon emission policy implementation among mining companies in Ghana. We adopted 5-point Likert scale statement questions. The questionnaire survey was conducted among Ghana’s four large-scale gold mining companies. As the sample number of available companies is limited, we used a descriptive analysis to assess the responses. The results show that the companies expressed widely different ways to respond to the issues of cost, a government policy for carbon emission reduction, and the acquisition of low-carbon technologies. Those companies that are relatively less prepared for this endeavor emphasized the uncertainties of securing sufficient energy in case of using low-carbon technologies. They also expressed a need for more trained workers to handle low-carbon technologies. We found that a major challenge among companies was the expected cost involved in implementing a low-carbon emission policy. There was a communication gap with the relevant government ministry about low-carbon mining options where the government should be able to facilitate the involvement of low-carbon technology providers, such as solar Photovoltaics installation.