Tons Of Carbon Emissions Research Articles

Economic, Environmental, and Social Value of Virtual Care in Otolaryngology: Sustainability in Quality Improvement Framework.

Apply the Sustainability in Quality Improvement framework to virtual care for Otolaryngology-Head and Neck Surgery (OHNS) patients to understand the economic, environmental, and social impacts. This project consisted of retrospective analysis of anonymized data from all appointments that took place in three academic ambulatory OHNS clinics (pediatrics, head and neck, and otology/neurotology) from fiscal years of 2021 to 2023. Data were obtained from our institution's Virtual Care Dashboard. The following metrics were calculated: travel costs avoided with virtual appointments (economic value), fuel and carbon emissions avoided with virtual appointments (environmental value), and differences in Ontario Marginalization (ON-Marg) Index scores between patients seen virtually versus in-person (social value). A total of 41,343 visits occurred over the 2-year period (18.1% virtual). Nearly all virtual visits were by telephone (99.6%). The average cost savings per virtual care visit was $87.50, and total cost savings across all 3 clinics was $640,300. Total environmental savings were 82,500 L of fuel and 246.6 metric tons of carbon emissions. There were no statistical differences in monthly average marginalization (ON-Marg) indices in patients seen virtually compared to in-person. Virtual care demonstrated financial and environmental savings for OHNS patients that can accumulate over multiple appointments. No difference in ON-Marg indices between patients assessed virtually versus in-person suggests that virtual care was accessible for patients regardless of social background. Our data suggests that virtual care may be a viable complement for delivering OHNS care that leads to fiscal and environmental savings for patients and ensures equitable access to care.

Carbon emissions reduction path of logistics industry in Hebei, China: based on internal factors

To explore the optimal carbon emissions reduction path for the logistics industry in Hebei Province, China, this paper considers internal factors such as logistics agglomeration, transportation structure, and technology innovation as regulatory measures. The system dynamics method is employed to simulate the effects of carbon emissions reduction for each factor. Then, based on examining the interactive relationships among these internal factors, the paper proposes the optimal carbon emissions reduction path for Hebei Province’s logistics industry under different sequences of implementing the three measures. The results indicate that logistics agglomeration, transportation structure, and technological innovation can contribute to varying degrees of carbon emissions reduction in logistics. By 2030, it is projected that logistics agglomeration will result in a reduction of 21,799 million tons of carbon emissions while transportation structure and technology innovation will lead to reductions of 23,459 million tons and 20,778 million tons respectively compared to the BAU scenario. Furthermore, intricate interactions exist among these three internal measures. The optimal path for reducing carbon emissions is TSI-AGD-LTI. Specifically, to optimize the transportation structure first, relieve the pressure of logistics carbon emissions aggravated by logistics agglomeration, promote the cross-regional transfer of logistics carbon emissions, and then improve the level of technology innovation to strengthen further the logistics carbon emissions reduction effect of transportation structure adjustment.

A study on the path planning and optimization of carbon peaking and carbon neutrality in the highway service area

The transportation sector is a major source of greenhouse gas emissions. Reducing emissions of the Highway Service Area (HSA) can help decrease the sector's carbon footprint and serve as a model for green transformation. Consequently, this study firstly employs the Life Cycle Assessment (LCA) to calculate the carbon emissions during the operational phase of a certain HSA in 2022, and then analyses the emission structure. Subsequently, four emission reduction pathways are identified, including Photovoltaic (PV) power generation, electrification retrofitting, forestry carbon sinks, and green roofs, in terms of both Three 100 Initiatives and carbon neutrality measures. On this basis, the planning of carbon peak and carbon neutral strategies for this service area under different scenarios are carried out, taking into account the dual carbon targets and relevant policies. The findings indicate that: (1) the HSA emitted approximately 766.84 tons of carbon emissions in 2022, with 96.53 % from purchased electricity. (2) PV power generation offers the optimal emission reduction benefits among four emission reduction pathways. (3) In carbon peaking scenarios, initiating PV construction is the optimal emission reduction strategy. (4) In most carbon neutrality scenarios, the simultaneous implementation of PV construction and bamboo planting can meet the neutrality requirements by 2060. However, if the HSA continues to develop economically after 2030, the economy could grow until 2035 to meet neutrality requirements. These findings can guide emission reduction strategies for other HSAs, accelerating the sector's transition to low-carbon emissions while supporting the development of policies for zero-carbon HSAs.

The green bonus of tax incentives: Evidence from China

Using a novel dataset covering over one million firm-level energy consumption entries from 2007-2016, we employ a difference-in-differences approach to estimate the causal effect of tax incentives on carbon emission intensity by exploiting China’s staggered implementation of accelerated depreciation policies for fixed assets in 2014 and 2015. We find that the accelerated depreciation policy leads to a significant 9.19% reduction in carbon emission intensity, equivalent to 0.11 tons of carbon emissions per 10,000 RMB of industrial value added. The policy effect is more pronounced for large firms, those with medium-term assets, and in regions with greater market integration and industrial agglomeration. Our firm-level analysis reveals that the policy reduces energy consumption, promotes a shift towards a cleaner energy portfolio and increases R&D investments in abatement technologies, as well as climate-friendly innovations. The above results show high consistency with verification at the aggregation level using satellite-based observations. Our findings highlight the potential of well-designed fiscal policies to contribute to global climate action efforts in developing economies.

Institutional Conversion to Energy-Efficient Ultra-Low Freezers Decreases Carbon Footprint and Reduces Energy Costs.

Introduction: The storage of biospecimens is a substantial source of greenhouse gas emissions and institutional energy costs. Energy-intensive ultra-low temperature (ULT) freezers used for biospecimen storage are a significant source of carbon emissions. ENERGY STAR-certified ULT freezers have the potential to decrease the carbon footprint. Objective: Quantify the impact of an institutional-scale freezer conversion program on carbon emissions and energy costs. Methods: A ULT freezer energy use prediction model was developed to identify and replace the most inefficient freezers in the research building for this pilot, and eventually institution-wide. Multiple linear regression factors included the number of years of use, storage volume, and ENERGY STAR certification status. Electrical usage and carbon emissions were quantified before and after replacement with ENERGY STAR models. Logistical methods were developed to decrease the risks of exposure of frozen samples to ambient temperature during content transfers. Institution-wide energy costs were derived by converting electrical burden to electrical costs. Carbon footprint assessment from ULT freezer operation was computed using the U.S. EPA Greenhouse Gas Equivalencies Calculator. Results: The pilot project revealed an annual reduction of 310,493 kilowatt hours of electrical usage, equivalent to 134 metric tons of carbon emissions. Annual electrical costs were reduced by $55,889 resulting in an 8-year payback on the initial investment. Using the pilot results, we modeled the benefit of the freezer exchange across the entire institution. The modeling predicted that conversion of the institution's remaining 1119 conventional ULT freezers to ENERGY STAR models would lower annual electrical usage by 7,911,549 kilowatt hours (3423 metric tons of carbon emissions), resulting in savings of over $1.4 million annually. Conclusion: Our methods make a large-scale initiative to replace energy-inefficient ULT freezers logistically possible, reduce carbon footprint, and demonstrate an attractive return on investment while proactively protecting valuable research materials.

Cooking-related thermal comfort and carbon emissions assessment: Comparison between electric and gas cooking in air-conditioned kitchens

The global shift to cleaner cooking methods is gaining momentum, with many countries considering electricity as a viable alternative to reduce carbon emissions. Cooking raises temperatures and humidity in residential kitchens, impacting comfort and increasing energy consumption. This study employed scenarios with and without kitchen air conditioning and focused on the comparative benchmark between electric cooking and gas cooking through an experiment on the unifying cooking heating effect (CHE), meaning that the heat gained by the substance in the pot was the same for both methods. Subsequent experiments explored thermal comfort under different conditions. The findings revealed that air conditioning notably enhances the thermal environment during electric cooking compared to gas cooking. At 21 °C with air conditioning for gas cooking, the overall thermal sensation vote (TSV) increases by 0.827 units (a 68.9 % rise), while the thermal comfort vote (TCV) decreases by 1.74 units (an 89.7 % reduction) compared with electric cooking at 25 °C. Furthermore, carbon emission analysis demonstrated the clear advantage of electric cooking over gas, which showed a 25 % reduction (0.50 kgCO2/h) in Shanghai. Considering cooking only, and assuming a 50 % shift to electric cooking in Shanghai's residential buildings, this could lead to a substantial reduction of 1.07 million tons of carbon emissions (a 9.98 % decrease) as well as 4.09 million tons of direct carbon emissions. These reductions account for 5.21 % and 19.99 % respectively of Shanghai's operational carbon emissions from residential buildings, respectively. A phased transition from gas to electric cooking is recommended to leverage these environmental benefits.

Assessment of techno-economic analyzes of grid-connected nuclear and PV/wind/battery/hydrogen renewable hybrid system for sustainable and clean energy production in Mersin-Türkiye

The main novelty in the presented paper is that it presents an energy analysis for a hybrid system that integrates nuclear power plants with wind/solar power plants for sustainable and clean energy production. In addition, excess energy is used to produce hydrogen. A techno-economic feasibility assessment is performed to ensure continuous electricity supply for hourly and peak residential load requirements in Mersin located in Türkiye. In the first stage of the study, suitable areas and sizes for solar and wind energy for energy production were determined with ArcGIS and Multi-Criteria Decision-Making methodologies. In the second stage, six scenarios were investigated in terms of electricity production, and excess energy was used for hydrogen production. The results brought out that more electricity was produced than the city's electricity consumption (5395 GWh) in all 6 scenarios considered. The maximum electricity production at a rate of 11900 GWh/yr and the maximum hydrogen at a rate of 132675 tons was produced in Scenario 4 with the cost of energy (0.31 $/kWh) and cost of hydrogen (12.6$/kgH2). In this scenario, the percentages of solar, wind and nuclear energy in total production 1 %, 12 %, and 87 %, respectively. According to the results, it was concluded that 236621 tons of carbon emissions were reduced when the city's energy needs were met according to the 6 scenarios in this study.

Empowering Sustainability: Floating Solar Photovoltaic Systems in Agriculture for Reduced Costs Carbon Emissions and Evaporation.

This study assesses the impact of implementing a floating solar photovoltaic system (FSPV) on the Turgutlu irrigation pond in Sakarya, Turkey, aiming to reduce energy expenses in agricultural irrigation and promote sustainability in farming. Two scenarios are developed to evaluate the FSPV, focusing on CO2 emissions mitigation, energy generation potential, evaporation reduction, conservation of terrestrial land, effects on agricultural production, decreased reliance on fossil fuels, and associated costs and return on investment (ROI). In the first scenario, the FSPV is expected to generate 7168MWh of energy, preventing the emission of 4520 tons of carbon, and reducing annual evaporation by 6686 m3. In the second scenario, the FSPV's energy output is estimated at 99MWh, preventing 64.2 tons of carbon emissions, and reducing annual evaporation by 94.4 m3. These findings provide valuable insights at the regional level, presenting a compelling case study for potential replication in other irrigated agricultural regions.

The time-advance effect of China's rooftop solar photovoltaics program on the dual carbon targets and its implication on the globe

Rooftop solar photovoltaics (RSPV) plays an important role in energy transition and climate goals. However, the contribution of RSPV to the dual carbon targets (DCTs) has not yet been quantitatively investigated at the national or global scale. Here, we investigate this contribution with an improved Stochastic Impacts by Regression on the Population, Affluence, and Technology (STIRPAT) model. We find out the time-advance effect of China's pilot RSPV program, i.e., doubling expansion of the current pilot area helps that the DCTs will be achieved 5 years ahead of schedule; a full expansion plan from merely six identified key provinces can be enough to guarantee that China will achieve carbon peak and carbon neutrality 5 and 4 years ahead of their respective time schedules. This effect implies global RSPV development; about 25 % of the world's buildable rooftops for RSPV installation will help reduce 1.4 billion tons of carbon emissions per year and thus achieve global carbon neutrality by 2050. The results will attract a widespread new energy community interested in expanding RSPV and helping governments formulate carbon reduction policies.

Cryptocurrency blockchain and its carbon footprint: Anticipating future challenges

This study investigates the environmental impact of cryptocurrency blockchain technology, specifically focusing on the adoption of Bitcoin and Ethereum. Utilizing instrumental regression analysis, it examines the consensus mechanisms—Proof of Work (PoW) and Proof of Stake (PoS)—and their association with carbon footprint. The findings reveal a clear correlation between the adoption of PoW-based cryptocurrencies like Bitcoin and carbon emissions. Notably, PoW-based cryptocurrencies generate approximately 0.86 metric tons of carbon emissions per transaction, which is significantly higher than PoS-based transactions like Ethereum. Concerns also arise regarding the sustainability of Bitcoin mining after the fourth halving period and potential shortages in PoW-based mining resources. As mining rewards decrease, the challenge of timely block creation intensifies. By projecting future BTC mining phases, the study anticipates diminishing rewards in each halving period, potentially leaving around 2500 BTC units available for mining by 2120. This suggests that achieving the full 21 million BTC may prove practically unattainable within the current blockchain framework. Furthermore, the research highlights the speculative and risky nature of cryptocurrencies lacking regulatory oversight, which tends to attract irresponsible investors. It stresses the importance of developing a nuanced understanding of the environmental implications of PoW cryptocurrencies and advocating for responsible innovation. Additionally, the study underscores the necessity of considering the monetary policy implications and raising awareness among investors to ensure responsible decision-making.

Analysis of decarbonization path in New York state and forecasting carbon emissions using different machine learning algorithms

The state of New York admitted 143 million metric tons of carbon emissions from fossil fuels in 2020, prompting the ambitious goal set by the CLCPA to achieve carbon neutrality. The paper focused on analyzing and predicting carbon emissions using four different machine-learning algorithms. It examined emissions from fossil fuel combustion from 1990 to 2020 and validated four different algorithms to choose the most effective one for predicting emissions from 2020 to 2050. The analysis covered various economic sectors including transportation, residential, commercial, industrial, and electric power. By analyzing policies, the paper forecasted emissions for 2030 and 2050, leading to the identification of different pathways to reach carbon neutrality. The research concluded that in order to achieve neutrality, radical measures must be taken by the state of New York. Additionally, the paper compared the most recent data for 2021 with the forecasts, showing that significant measures need to be implemented to achieve the goal of carbon neutrality. Despite some studies assuming a trend of decreased emissions, the research revealed different results. The paper presents three pathways, two of which follow the ambitious plan to reach carbon neutrality. As a result, the emission amount by 2050 for the different pathways was projected to be 31.1, 22.4, and 111.95 of MMt CO2 e, showcasing the need for urgent action to combat climate change.

Grey prediction of carbon emission and carbon peak in several developing countries

The carbon emissions in BRICS countries (Brazil, Russia, India, China and South Africa) account for nearly half of global carbon emissions. To explore the future carbon emissions in BRICS countries and the carbon peak in China and India, the grey Gompertz model with new information priority accumulation is proposed. Intelligent algorithms are used in the parameter optimization. The new model not only increases the influence weight of new information on the predicted results, but also changes the predicted trend of traditional grey prediction model. The effectiveness of the new model for the carbon emission forecast is verified through the known data set. According to the predicted results, the carbon emissions in Russia, Brazil, South Africa tend to stabilize over the long term. The carbon emissions in China and India are rising slowly. China will achieve the carbon peak in 2029 with 10.2 billion tons of carbon emissions. India will not achieve the carbon peak by 2030. The current situations of the carbon emissions in BRICS countries are analyzed. It is concluded that the energy structure, industrial development and the urbanization need to be paid attention to. Detailed policies and suggestions are given in the fourth section. The above research results indicate that the new model can predict the limit value of the carbon emission. The predicted results can provide data support for the policy formulation in carbon reduction.

A Study on Consumer’s Perception on Green Home Appliances

A household is the building block of the society and home appliances are the main power consumers in a household. Hence, they are the best means to save energy at a household level and achieve a reduction in per capita power consumption. If the household units save energy, the nation saves the energy and thereby the ideal place to initiate the green change at the consumer level is through the home appliances sector. Appliances more than 10 years old are clunkers as they use up most of the energy. If consumers replace them with energy efficient appliances, it will not only save billions of kilowatts of energy annually but also billions of tonnes of carbon emissions globally. Most of the home appliances have a longer life. Hence its medium to long term benefits should be weighed while making a purchase decision. “Despite various green initiatives and a vibrant consumer electronics market, consumer adoption of green home appliances is still lagging behind contributing to the high-power consumption and monetary loss to the individual and the country. The study made an attempt to understand consumer understanding, perceptions and acceptance of Green Home Appliances.

Digital twin-aided transfer learning for energy efficiency optimization of thermal spray dryers: Leveraging shared drying characteristics across chemicals with limited data

Efficient energy management is crucial for spray-drying units as it can substantially improve product yield, reduce operating costs, and enhance energy utilization. However, due to limited data problems, the monitoring performance of the energy efficiency of a model is inefficient and unreliable, making it difficult to adjust operating conditions and hindering effective utility management. Therefore, this study proposes a long short-term memory-based transfer learning model with shared source-target characteristics for enhancing energy efficiency trackability under limited efficiency labels. Utilizing a long short-term memory structure improves the capability of capturing the process dynamic behavior. Synchronously, the digital twin-aided transfer learning concept supports the model by leveraging the parameters learned from the simulated source domain to assist the performance of the model in a limited data domain with different chemicals. The reliability and accuracy of the model are verified by a real industrial case study involving the detergent powder drying process. Results show that the model testing achieved an r-squared value of 0.938, outperforming conventional techniques by boosting the performance of the network up to 14.53 % and reducing surplus energy on demand and supply by 50.05 % and 81.27 %, respectively. The proposed model reveals the interconnection between source and target accuracy and provides a reliable learning process of the target domain observed based on the distribution of the testing performance. Notably, the model deployment indicates a considerable decrease of 16.63 % in natural gas consumption, leading to an enhancement of 11.92 % in evaporation efficiency and the prevention of 483 tonnes of carbon emissions annually.

Углеродные кредиты в стратегиях декарбонизации нефтегазовых компаний

Carbon credits are an increasingly important tool for decarbonization. They are of particular importance to oil and gas and other energy companies for which burning fuel comprise the core of the business. The two main sources behind carbon credit emission are renewable energy projects and projects to sequester greenhouse gas emissions from natural systems, mainly forests. Without offsetting mechanisms, including nature-based solutions, companies with high carbon footprint are fundamentally unable to achieve net carbon neutrality. Despite challenges, the voluntary carbon credits markets continue to grow. In 2020–2022 the world’s 34 largest companies by capitalization bought certificates equal to 38 million tons of carbon emissions, which is equivalent to the combined greenhouse gas emissions of Ethiopia and Kenya. 50% of these credits were purchased by oil and gas companies and 28% by automobile concerns. In the near future, voluntary carbon markets will continue develop in an imperfect mode of trial and error, which is inherent to all markets at their early stages of development. The author shares the view that a large-scale non-ideal credit/offset market will be more conducive to solving the strategic task of decarbonizing economic growth than an ideally organized but small-scale market. In principle, this position is supported by authoritative regulators, whose policy is critical for the development of a new market. In May 2024, key economic departments of the US government, including the Department of Agriculture, the Department of Energy and, most importantly, the Department of the Treasury, unequivocally supported the development of a carbon credit market that promotes decarbonization, opens up new opportunities for investment in climate projects and enables realization of wide externalities that positively influence economic growth and preservation of biodiversity.

Can Solar Photovoltaic Poverty Alleviation Policies Reduce Carbon Emissions and Increase Income in China?

China implemented a solar photovoltaic (PV) poverty alleviation (PVPA) policy of building nearly 0.24 million PVPA power plants in 2014-2020 to fight poverty. However, our current knowledge of its effects, encompassing not only primary poverty alleviation but also secondary objectives such as carbon emission-reduction, remains comparatively constrained. Here, we present a comprehensive assessment of the emission-reducing and income-increasing effects of the PVPA policy using estimated carbon emission factors and a staggered difference-in-difference model based on integrated data from almost all actual PVPA plants in China. Our analysis revealed the co-benefits of emission-reduction and poverty alleviation, with PVPA policy boosting villagers' per capita net income by 2-3% in villages with PV plants. A nonlinear, inverted U-shaped relationship between income and PVPA plant investment was identified with a $2.21 million inflection point. Spatial heterogeneity was observed in the income-increasing effect, with centralized village-level plants proving more effective than rooftop household plants. China's PVPA plants reduced carbon emissions by nearly 3% in 2020 and are projected to generate 774 billion kW h of electricity by 2045, mitigating 715.75 million tons of carbon emissions. The findings from this research offer insights for optimizing antipoverty and climate change policies to facilitate sustainable development goal achievement in China and other developing nations.

Building and carbon emissions: Evidence from real estate purchase restriction policy

Carbon emission reduction in the building sector plays an essential role in the global mitigation effort, and a policy of limiting the expansion of buildings could be a critical intervention. Previous studies mainly focused on the process and materials in the building sector to discuss ways of building decarbonization rather than the restriction of building expansion. The Real Estate Purchase Restriction policy in China produced powerful enforcement to control the disorder expansion of building construction in China. Using the difference-in-differences model, this study finds that the policy caused approximately 5.7 million tons of carbon emissions decrease per year in the pilot cities, which could save about 52.55 million dollars social cost of carbon. Through the examination of the impact mechanism, we found that the implementation of real estate purchase restriction policy contributed to carbon emissions' reduction via reducing population growth and reducing freight volume.

Reducing carbon footprint and promoting resource sustainability in the retail industry through the prevention of dead stocks

In the retail industry, majority of the traditional inventory management methods commonly focus on minimizing business costs but fail to consider implications on environmental sustainability. Existing inventory classification methods have failed to consider the interaction among the different performance indicators for a timely recognition of the potential dead stocks and dead stocks in the inventory. This study aims to identify and effectively classify potential dead stocks and dead stocks through an enhanced inventory classification method that integrates the remaining product shelf life as an indicator for timely recognition of dead stocks. The study also aims to provide guidelines in setting up decision rules needed in objectively classifying inventory based on each performance indicator through an automated algorithm with decision rules. Using data sets taken from open databases that provide real-world data, results show that the automated algorithm can effectively detect the presence of dead stocks and distinguish such inventory from potential dead stocks in the system which existing inventory classification methods are unable to do. One-Way ANOVA Tests performed showed that the proposed classification method can consistently classify inventory across different retail data sets while Monte Carlo simulation was used to simulate the amount of inventory that becomes dead stocks in a multi-period inventory management system. Results show that 17% of these potential dead stocks, if undetected, become dead stocks in the following period. Over a ton of carbon emissions is prevented with lesser dead stock waste disposal. The value of this study lies in the fact that classifying inventory to detect the presence of potential dead stocks is an essential first step in solving the dead stock problem not just for business profitability but also for sustainable resource consumption and a sustainable environment.

Energy saving potential analysis of a short cycling industrial air compressor in a marine equipment manufacturing plant in Türkiye

Compressed air systems are recognised as significant energy users and are characterised by their notably inefficient energy consumption. This ensures their significance and potential for decarbonisation through cleaner and more responsible energy consumption in manufacturing facilities, such as marine equipment manufacturing plants in the shipbuilding industry, in order to address the various economic and regulatory challenges related to energy use and climate change. In the relevant literature, there are numerous studies on various energy-saving measures for compressed air systems; however, none concentrates on the problem of short-cycling phenomena and associated energy-saving potential. In this study, using a novel and systematic energy audit methodology, a detailed energy audit of a rotary-type screw air compressor was conducted at a marine equipment manufacturing plant in Türkiye. The systematic energy audit methodology was based on the measurement of power consumption and the evaluation of various operation parameters to assess the existing performance of the compressor, including compressed air demand, compressed air production, cycle speed, air tank volume, specific capacity, and duty cycle. The audit results revealed that the air compressor was short cycling, resulting in excessive energy consumption. Comprehensive technical and economic assessments were conducted to determine the root cause of the compressor's short cycling and to identify energy-saving potentials. It was determined that the compressor was oversized relative to the plant's compressed air demand patterns, while the air tank was inadequately sized, causing the compressor to engage in short cycling. To replace the existing short-cycling compressor, a scenario analysis revealed that the deployment of an optimised system consisting of a fixed-speed baseload compressor and a variable-speed trim compressor can reduce the plant's energy consumption for the compressed air system by a significant 73%. This results in annual energy savings of 74,160 kWh, annual cost savings of €9.373,8, and an annual reduction of approximately 49,9 tonnes of carbon emissions. This application requires an initial investment of €24.280 and is anticipated to redeem itself in 2,2 years. Moreover, it is anticipated to generate a net present value of €147.602 over its 20-year lifespan.

Spatial Patterns of Solar Photovoltaic System Diffusion Kisumu County, Kenya

Purpose: This research aligns with Sustainable Development Goal 7, contributing to the progress outlined in the 2030 Agenda for Sustainable Development and the commitments of the Paris Climate Agreement. Specifically, this study focuses on the spatial analysis of solar photovoltaic (PV) systems, offering valuable insights for academic exploration and informing public policy decisions related to the widespread adoption of this increasingly vital renewable energy technology. The outcomes of this project transcend academic significance, extending to practical applications for energy practitioners, policymakers, academics, and future researchers. The meticulous tracking of solar PV system spatial patterns in Kisumu County yields data that not only benefits its residents but also serves as a valuable resource for the entire nation. This information will be instrumental for current energy practitioners, policymakers, academicians, and prospective researchers seeking to advance the collective knowledge in this field.
 Methodology: The study adopted a Quasi-Experimental research design to explore various social phenomena, aiming to identify key facts. Utilizing statistical evidence, we conducted numerical comparisons and statistical inferences to validate or refute the research questions. Locational information on households utilizing small home systems was extracted from a secondary Solar Database. This data underwent georeferencing, enhancing our comprehension of the actual geographical distribution of households and facilitating the achievement of our research objectives. In the process of data analysis, we employed inferential statistics, specifically regression analysis, conducted using ArcGIS PRO powered by ESRI. The utilization of ArcGIS Pro extended to the creation of an empirical model. This model was designed to probe into the factors influencing the observed spatial diffusion patterns, providing a robust analytical framework for our investigation.
 Findings: In the initial objective, cluster and outlier analysis unveiled a distinct low-high cluster pattern for solar home systems (SHS). The optimized hotspot analysis consistently identified SHS hotspots and cold spots within the region, particularly aligning with urban areas, notably Kisumu. The second objective exposed factors influencing diffusion, revealing negative correlations with population density, household density, and poverty rate, indicating diminished adoption in densely populated and impoverished areas. Conversely, positive correlations with income, education, and electrification rates signaled heightened adoption in wealthier, educated communities. Despite consistent diffusion trends, an empirical model underscored the substantial impact of income and electricity on SHS diffusion. The third objective disclosed that between 2016-2021, SHS diffusion contributed to the mitigation of 268,581.6 metric tons of carbon emissions.
 Unique Contribution to Theory, Practice and Policy: This research makes a distinctive contribution to theory by delving into the impact of solar home systems (SHS) in Kenya, particularly within the context of the country's commitment to reduce greenhouse gas (GHG) emissions. The theoretical foundation lies in addressing the existing gap in understanding the spatial distribution and diffusion patterns of SHS and their role in GHG reduction, aligning with Kenya's focus on renewable energy adoption.