Sector Emissions Research Articles

Multi-criteria-based evaluation of digital Carbon Footprint tools from a household user perspective.

Greenhouse Gas (GHG) emissions significantly impact the environment and human health, but many people are unaware of how their daily choices affect climate change. Carbon calculators estimate emissions in sectors like construction, digital health, transportation, and food production. However, they are still developing and face challenges that require attention from both industry and academia. Researchers highlight the absence of a unified framework for assessing these tools, leading to simplistic binary evaluations, which result in inaccuracies in household tool evaluations. In the current study, an assessment framework has been created by integrating existing binary frameworks and incorporating the MCDA method for weighted multi-criteria evaluation, offering a quantitative scoring system for qualitative criteria. This dual-criteria framework addresses both practical and academic aspects of the evaluated tools. The framework was used to evaluate 15 tools chosen for their prominence in search engine results and their impact on scientific publications. Findings revealed that tools such as Svalna, WWF calculator, and Carbon Savvy produce comparable results, while others, like the UN CF calculator, show deviations up to 136 tons. The study highlights challenges related to feasibility, availability of resources, and user engagement, noting that the criterion for accessibility for special groups received a score of zero across all CF tools. The work is expected to yield enhanced understanding that will aid in the selection of appropriate CF tools, foster sustainable practices, and offer developers direction to ensure their designs conform to established standards.

An input-output approach to study the impact of carbon taxes in (almost) every country

Some forty carbon tax initiatives have been adopted, and more countries are evaluating the possibility of implementing this instrument every year. However, there is significant heterogeneity in tax rates, type of covered emissions, and regulated sectors, which is explained by the different levels of environmental concern and cost that each country is willing to assume. In this context, the present study simulates various scenarios of carbon taxes in (almost) every country in the world to contribute to the global design of climate change policies. Specifically, input-output tables and sectoral CO2 emissions of each country are used to calibrate the environmental extension of the Leontief price model, obtaining the impacts on prices, production, and emissions at the sectoral, national, or global level. The results show that the same effective tax rate can have very different effects in each country, mainly explained by the composition of the energy matrix. In addition, global CO2 emissions would be reduced by 0.10% ∼ 0.23% for each dollar of CO2 tax applied to all sectors and countries. Finally, a more outstanding commitment from rich countries and larger developing countries is required to contribute more decisively to combating climate change in the short term.

3DVar sectoral emission inversion based on source apportionment and machine learning

Air quality models are increasingly important in air pollution forecasting and control. Sectoral emissions significantly impact the accuracy of air quality models and source apportionment. This paper studied the 3DVar (three-dimensional variational) emission inversion method, which is based on machine learning, and then expanded it to sectoral emission inversion combined with source apportionment. Two machine learning conversion matrices were established to implement this method: a matrix that converts the total pollutant concentration to sectoral source apportionment results and a matrix that converts the sectoral source apportionment results to corresponding emissions. Combined with the O3 (ozone) concentration contributed by VOCs (volatile organic compounds) and NOx (nitrogen oxides) precursors in source apportionment, the inversion ability for O3–NOx–VOCs nonlinear processes was improved. Taking the BTH (Beijing‒Tianjin–Hebei) region from January 15 to 30, 2019, as an example, the results revealed that the regional errors of PM2.5 and O3 in the inversion experiment were reduced by 47% and 45%, respectively, and the temporal errors were reduced by 44% and 16%, respectively.

Navigating the complex policy landscape for carbon farming in The Netherlands and the EU

Background The EU carbon removal and carbon farming (CRCF) framework sets the quality requirements for voluntary greenhouse gas certification schemes for i) permanent removal, ii) temporary carbon storage in long-lasting products, iii) temporary carbon storage from carbon farming, and iv) soil emission reduction. The next step is to develop “tailored certification methodologies for different types of carbon removal activities.” Via CRCF compliant certification schemes, certificates can be issued and sold to generate extra funding for carbon farming via voluntary carbon markets or other (future) performance-based incentive schemes (e.g., Agri sector Emissions Trading Scheme). Method We provide a qualitative assessment of the complex landscape of carbon farming incentive schemes and their associated monitoring, reporting, verification, and certification (MRV-C) frameworks. To illustrate these complexities, we take the farmers’ perspective of a Dutch dairy farmer that wants to implement different carbon farming activities (i.e., a portfolio), while aiming to secure a decent farm-level income. We discuss the farmers’ challenge by analyzing different options and routes for the financial valorization for several carbon farming activities. Results We find that the incremental MRV-C and incentive schemes, like the EU CRCF and potential agri-ETS are likely to add to the existing administrative burden for (carbon) farmers in the EU. Proper alignment, integration and standardization of the EU CRCF with existing MRV-C infrastructure(s) is needed to limit the administrative burden and transaction costs. Also, there is a need to provide better ex-ante clarity to (carbon) farmers and investors on the complementarity of incentive schemes from different policy domains (e.g., agriculture, climate, energy, bioeconomy). Conclusion We argue that the complexity of the MRV-C and incentives landscape ads to the administrative burden and flag this a potential source of inertia for investments in carbon farming. We conclude with recommendations to simplify and streamline the policy mix and MRV-C processes.

Decomposition of Carbon Dioxide (CO2) Emissions in Hungary

Elevated levels of global greenhouse gases (GHGs), primarily produced through fossil fuel combustion, present environmental risks such as prolonged droughts, global warming, and catastrophic floods. Despite raising awareness and international efforts to combat climate change, fossil fuel and GHGs demand persists. At this juncture, this study investigates Hungary's transportation sector, a notable CO2 emitter. Using the Kaya Identity and Log Mean Divisia Index method, this study decomposes the primary components of CO2 emissions in Hungary's transport sectors from 2001-2021. This analysis is done in two decomposition levels, annual and periodic decomposition analysis for each of the five years starting from 2001. The analysis reveals that the economic activity effect ∆CGPE is the predominant driver of CO2 emissions, while the energy intensity effect ∆CEIE improvements have substantially mitigated these increases. Population changes affect ∆CPOPE, and the carbon emission intensity effect ∆CCEIE has had minimal impacts. The study provides insights and recommendations for policy and strategic interventions to reduce emissions and promote sustainable transportation practices in Hungary.

Operational long-term management of a salt cavern for industry targeted green H2 production

Hydrogen and its derivatives such as ammonia are increasingly important for reducing carbon emissions in sectors with limited alternatives. Large production systems are generally preferred to reduce costs and for those, advanced management is a cornerstone to the project economic viability. The present work explores the development of an Energy Management System for an industrial low-emission ammonia production platform. The latter is connected to the electrical grid and consists of 1.5 GW of renewable production units, around 500 MW of electrolyzers and storage facilities including especially a salt cavern for H2 storage. The long-term management of the salt cavern is decisive to enhance the profitability while satisfying industry related constraints such as the carbon content of the produced H2. The literature lacks in addressing both aspects simultaneously. Methodological approaches to tackle the long-term management and industrial constraints are proposed and compared. For the considered platform, it is shown that a strategy based on precalculated cost functions in the long term horizon allows relative gains of 2.5% with respect to a state-of-the-art short term management leading to an annual cost saving of approximately $6.2 million in the platform operational expenses. This translates to a reduction of the gap between the state-of-the-art and the optimal a posteriori solution by about 82%.

(Non-)terrestrial and (Non-)local pathways of behavioral policy diffusion in European cities’ climate action plans: Contextual, cultural, and leadership framing

This study investigates the spread of innovative behavioral (green nudging) policies within city-level Climate Action Plans (CAPs) across the European Union, focusing on how these innovations diffuse and the factors influencing their adoption. Using textual analysis with a dataset consisting of CAPs from 40 cities across Europe, we categorized various green nudging innovations and then tracked their origins and uptake. Then, we employed fsQCA (Fuzzy set qualitative comparative analysis) to identify the key factors driving diffusion. The findings reveal that while certain innovations, particularly in the building and transportation sectors, have achieved widespread adoption, other initiatives like community co-creation and urban parks have seen lower diffusion. Local terrestrial factors, especially sectoral carbon emissions, are significant drivers, with cities facing higher emissions more likely to adopt these policies. Interestingly, local emissions levels and strong climate leadership emerge as more critical determinants than economic status or climate similarities. The study identifies two primary diffusion pathways—Cultural Leadership for Emission Reduction and Local Adaptive Synergy—demonstrating the diverse strategies cities employ based on their unique contexts. This research highlights the importance of expanding green nudging measures in CAPs beyond technological and infrastructure domains to promote low-carbon behaviors comprehensively.

Research on ozone pollution control strategies for urban agglomerations based on ozone formation sensitivity and emission source contributions

Despite recent enhancements in China's anthropogenic emission controls, ozone (O3) concentrations have continuously increased owing to its nature as a secondary pollutant and the complexities of its production and consumption processes. This study quantified the contributions of urban and sectoral cross-emission sources to O3 levels and identified the anthropogenic emission sources requiring targeted control. Moreover, O3 sensitivity tests were conducted to determine optimal reduction ratios for nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions. The results were used to recommend effective measures for controlling O3 pollution in the Central Plains urban agglomeration (CPUA). The top 35 cities and sectoral cross-emission sources accounted for 80% of the O3 concentrations in the region, indicating the need for prioritized management of these sources. To achieve reductions in O3 concentrations across all cities, it was found that a 10% reduction in total NOx emissions would require a minimum of 18% reduction in VOCs emissions. Our results indicated that the appropriate coordination of reductions in VOCs and NOx emissions reduced the maximum daily 8-h average O3 (MDA8) concentrations in CPUA by 0.14%–4.78%. Enhancing control measures for prioritized emission sources reduced MDA8 concentrations by 0.78%–7.09%. Furthermore, adjusting the production and emission hours of the industrial sector resulted in a decrease in MDA8 concentrations by 1.10%–12.62%. Overall, our findings indicate that appropriately coordinated reduction of precursor emissions can reduce O3 levels. Further efforts to mitigate O3 pollution should include optimizing the timing of emissions from the industrial sector and other major sources of VOCs emissions.

Klímasemleges energiatermelés – Kilátások Magyarországon 2024-ben

In order to protect our planet's climate, it is important to reduce humanity's carbon dioxide emissions significantly. The energy sector is one of the most polluting industries, but the rapid spread of renewable energy sources over the past 20 years can significantly contribute to the reduction of carbon dioxide emissions. The study examined which energy sources are used to produce electricity in Hungary, and how the proportion of renewable energy has developed in recent years compared to more polluting energy production methods. There is a strong negative exponential relationship between the share of renewable energy and the carbon dioxide emissions of the energy sector, based on which the carbon dioxide emissions of the energy sector can be significantly reduced by increasing the share of renewable energy. A forecast was made for the renewable share based on past data and the revised state of the National Energy and Climate Plan in 2023, and a linear forecast was made for the energy sector's emissions. There is a strong exponential relationship between the data series. Both models examined in the study had strong explanatory power, but neither model supports the possibility of climate-neutral energy production by 2030.

ПРИОРИТЕТЫ ТЕХНОЛОГИЧЕСКОЙ МОДЕРНИЗАЦИИ ДЛЯ ДОСТИЖЕНИЯ УГЛЕРОДНОЙ НЕЙТРАЛЬНОСТИ ЭКОНОМИКИ КАЗАХСТАНА

This article explores the directions for improving the technological development of Kazakhstan in light of the implementation of the Strategy for Achieving Carbon Neutrality by 2060. By studying international experience and assessing the level of technological development and scientific achievements of the country within the context of global trends, the authors identify key problem areas and barriers that hinder the fulfillment of national commitments in researching technologies to reduce greenhouse gas emissions in the country's basic economic sectors. The article concludes by justifying the priority paths and mechanisms for the technological modernization of the national economy as a key driver for Kazakhstan's transition to low-carbon development. Keywords: Technological modernization, carbon neutrality, decarbonization of basic sectors, industrial-innovative development, energy efficiency, greenhouse gases.

Cost, innovation, and emissions leakage from overlapping climate policy

Jurisdictions have implemented a variety of policy instruments to mitigate greenhouse gas emissions. However, interactions between overlapping climate policies can lead to unintended impacts. This study demonstrates how interactions between an incomplete emissions cap and additional climate policy can result in higher emissions and higher average abatement costs relative to an emissions cap alone. This sectoral policy emissions displacement effect is then quantified through simulations using a computable general equilibrium model for the case of California's low-carbon fuel standard (LCFS) and cap-and-trade program. Emissions increase as a result of the LCFS incentivizing greater production of alternative transportation fuels with emissions not covered by the emissions cap. Emissions leakage can be mitigated by incorporating elements of a fixed-price instrument (i.e. carbon tax) to improve policy complementarity or requiring an obligation for the lifecycle GHG emissions of fuels under the emissions cap.

Inverse modelling approach to assess air pollutant emission trends, and source contributions in highly polluted cities

Pollutant concentrations are regularly measured in many cities, but emission loads often remain unknown and are irregularly estimated. This paper presents a methodology to estimate total emissions in a highly polluted megacity using an inverse modeling approach. An inverted Fixed Box Model (FBM) has been used with pollutant concentrations and meteorological variables to estimate emissions of pollutants by discounting the meteorological variability from the measured concentrations. Model-predicted emissions are validated with existing inventories and then used to assess annual, monthly, and diurnal trends in emissions. The applicability of the model is demonstrated for Delhi. Trend analysis shows that pollutants are emitted in a unique pattern that also differs over time periods of consideration. Yearly trends in Delhi show that PM10, NOX, and SO2 emissions have increased by 33%, 4%, and 16%, respectively, during 2008–2018. However, due to controls, the emission intensities have been found to stabilize for PM10 and decrease for NOX and SO2 pollutants in Delhi. Monthly trends indicate that summer months have higher emission rates owing to intensive energy demands (in automobiles, power plants), dust storms, and increased re-suspension of dust due to higher wind speeds. However, due to dispersive meteorology, ambient concentrations are lower in summers. The inverse modeling shows that PM10 emissions are dominated by dust sources (73%), while PM2.5 has the largest contribution from transport sector emissions (34%). NOX and SO2 emissions in the city are contributed heavily by the transport (84%) and industrial (92%) sectors, respectively. The model helps in understanding temporal emission trends and source contributions, which can be useful to implement effective control measures and track the progress of control.

Deciphering the point source carbon footprint puzzle: Land use dynamics and socio-economic drivers

Point source carbon emissions account for approximately 80 % of total emissions. Investigating the influence of land use and socio-economic indicators on these emissions is crucial for achieving sustainable development goals. Existing research faces challenges such as focusing on specific regions, mixing variables that may exhibit multicollinearity, and lacking sufficient land use information. This study takes China, the largest emitting country, as a case study, utilizing geospatial big data to subdivide land use into 11 categories based on emission sectors. The impacts of land use and socio-economic indicators on different emission sectors are discussed from the perspectives of bivariate and spatial statistical analysis, with spatial hotspots identified. Hierarchical regression is used to evaluate the explanatory power of the indicators and to establish models, and potential carbon reduction strategies are further explored. Key findings reveal: (1) Significant multicollinearity between land use and socio-economic indicators was demonstrated, with land use explaining 57.1 % of emissions compared to 37.4 % explained by socio-economic indicators. The spatial consistency between land use and emissions exceeds 80 %, and the spatiotemporal variability is relatively low, making land use a more advantageous factor in explaining point source carbon emissions. (2) Agricultural mechanization increases emission intensity, but this efficient farming method helps convert surplus plowland, the largest influencing factor (Coefficient = 0.717), into carbon sinks, thereby controlling agricultural emissions. (3) Land intensification helps control industrial land, the main factor influencing industrial emissions (Coefficient = 0.392). It also contributes to the efficient use of carbon reduction technologies and industrial supporting land. (4) Mixed commercial and residential land has the greatest impact on commercial, service, and household emissions. However, its relationship with the economy (Correlation = 0.479) is stronger than its relationship with emissions (Correlation = 0.182), making it more applicable to cities that serve as economic growth hubs.

Sectoral Size‐Resolved Particle Number Emissions With Speciation: Emission Profile‐Based Quantification and a Case Study in the Yangtze River Delta Region, China

AbstractSizes and number concentrations are critical parameters for the impact of atmospheric particles on climate and human health. However, comprehensive studies focusing on size‐resolved particle number (PN) emissions from various sectors are scarce. This study aims to fill this research gap by developing sectoral size‐resolved PN emissions for major species including sulfate, organic mass (OM), and black carbon (BC). The size‐resolved emission profiles derived from various measurements in the literature were integrated with a particle mass emission inventory (EI) for 13 major sectors in the Yangtze River Delta region of China as a case study. The particle number size distribution (PNSD) of emitted particles exhibited two distinct peaks: one at approximately 10 nm and the other in the range of 40–60 nm. The primary contributors to PN emissions in the region in 2017 were power plants, gasoline vehicles, diesel vehicles, and cooking sources. In terms of species, OM dominated PN emissions, followed by primary sulfate and then BC. A regional size‐resolved aerosol model employing the size‐resolved PN EI developed here (referred to as the BIN‐SPE experiment) provided reasonably accurate temporal variations of the total PN concentration and captured the PNSD within the size range of 10–300 nm. Uncertainty analysis of sectoral PN emissions across size ranges was carried out and the performance of the BIN‐SPE experiment was compared with those of three commonly used PN emission parameterizations. Our model evaluations highlight future needs for in‐depth investigations into more advanced size‐resolved emissions and secondary OM formation mechanisms.

Synergies quantification and evolution on air pollutant and CO2 emissions driven by different socio-economic effects

Realizing a synergistic reduction of air pollutant and CO2 emissions (APCE) is an important approach to promote a green socio-economic transformation in China, and it can provide a solid foundation for the achievement of clean energy production and climate action under a sustainable development goal framework. The objective of this study is to explore the quantitative relationship and evolution of synergies between APCE in industrial sectors driven by different socio-economic effects from 2007 to 2020 in China. The results indicated that the main sectors of pollutant emissions had consistency, however, large differences in the reduction efficiency of emissions exist among pollutants. The efficiency in reducing CO2 emissions was about 48% lower when compared with reductions of SO2 (95%), NOx (86%), and smoke and dust (83%) emissions from 2007 to 2020. The effects of improved technology were the main contributor to a reduction in pollutant emissions, but the synergies between APCE driving by it were not achieved. While the synergies between APCE driven by structure and final demand effects were significant. The synergies between NOx and CO2 emissions were stronger driven by final demand structure and type effects, with correlation coefficients of 1.06 and 1.13, respectively. Besides, the degree of synergistic reduction between APCE in most industrial sectors was around zero. Therefore, the efficiency of synergistic pollution reduction should be improved with the development of a synergistic governance system for industrial sectors. The structural decomposition analysis based on input-output model combined with the cross-elasticity analysis method to quantitively synergies between APCE from the consumption (demand) perspective, considering the connections between industrial sectors with socio-economic developing, which would contribute to the industrial synergistic reduction and green transformation as the consumption driven gradually increasing.

The Application and Evaluation of the LMDI Method in Building Carbon Emissions Analysis: A Comprehensive Review

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals.

Climate strategies for oil and gas production under the lens of an Integrated Assessment Model: The case of Brazil

Future projections of oil and gas demand suggest that some production will remain necessary. Although attention often focuses on CO2 emissions from the combustion of their products, oil and gas production is also a relevant global emission source of both CO2 and CH4. Hence, understanding the carbon performance of upstream activities in producing nations is vital for distinguishing producers in a climate-pressured global market. This work explores climate strategies for the oil and gas upstream sector, using Brazil as a case study. The sector´s emissions profile is evaluated under distinct national climate scenarios. The analysis employs BLUES, a national Integrated Assessment Model (IAM), to access production volumes, mitigation measures applicable to the sector, and carbon dioxide removal potentials within the country to eventually offset the sector's remaining emissions. Results indicate sustained oil and gas production over the evaluated horizon (2020–2050) without compromising national climate goals, yet the sector's future emissions trajectory does not align with decarbonization targets pursued by more ambitious oil-producing nations and industry players. Despite sectoral mitigation measures indicated by the model, considerable emissions remain until 2050. Conversely, the country offers ample offsetting opportunities with potential synergies for the sector, especially through BECCS. Furthermore, the acceptability of offsets is discussed.

Accelerating power grid interconnectivity in Southeast Asia: a private sector perspective

Abstract Integrating the power systems between member states of the Association of Southeast Asian Nations (ASEAN) can facilitate economic growth for the region and enable businesses to cost-effectively procure reliable, clean electricity. As corporations strengthen their clean energy commitments, the grid becomes an important enabler of power sector emissions reductions as well as economic development. This paper offers perspectives from the Asia Clean Energy Coalition (ACEC) on the importance of renewing the Memorandum of Understanding (MOU) on the ASEAN Power Grid, focusing on accelerated implementation to expand multilateral power trade and cross-border grid interconnections. ACEC consists of clean energy buyers, sellers, and financiers that can drive economic growth. It supports strengthening the ASEAN Power Grid MOU to recognise the importance of holistic regional grid planning, prioritising interconnectors that scale up clean energy, harmonising regulatory and technical frameworks, and making data accessible to facilitate multilateral power trade and clean energy tracking.

Towards a Green and Inclusive Power Sector in the Greater Mekong Subregion

Abstract This paper examines the power sector and electricity market in the Greater Mekong subregion (GMS) and explores potential pathways for low-cost, low-carbon, and low-conflict development of the sector. The results show that implementing a smartly-designed regional power grid, combined with enhanced cross-border trade, can facilitate a transition to a 100 percent renewable electricity supply. It projects a three-fold increase in energy demand by 2050 and highlights the need for a transition to low-impact renewable electricity sources. It utilizes modelling tools to analyze four scenarios and assess their impacts on electricity cost, carbon emissions, and environmental and social aspects. Results indicate that a scenario combining an expanded regional grid and cross-border trade, increased share of non-hydro renewable energy sources and energy efficiency measures yields the best outcomes. This approach reduces sectoral emissions, lowers costs, and minimizes conflicts. The paper recommends establishing coordinated and functional regional institutions, ratifying a regional grid code, and promoting standardized cross-border power trade arrangements. In conclusion, the report highlights the relevance of a well-designed regional power grid and cross-border trade for a successful transition. Implementing the recommended measures can lead to a greener and more inclusive power sector, ensuring the region’s long-term energy security and environmental sustainability.

Benefits and challenges in deployment of low global warming potential R290 refrigerant for room air conditioning equipment in California

High global warming potential gases (“high GWP”) are the fastest growing sector of greenhouse gas emissions in the world and in California and are primarily used as refrigerant gases in refrigeration and cooling equipment. Hydrofluorocarbons (HFCs) refrigerants are the dominant type of high GWP gases with GWP values thousands of times larger than CO2 on a 100-year timescale. Refrigerant-grade propane (“R290”) has a very low GWP (GWP = 3.3) with good thermodynamic properties and good cooling equipment performance but the flammability of any leaked refrigerant makes equipment design, handling, and maintenance critical factors to manage. This paper focuses on the potential climate benefits and costs of transitioning to R290 refrigerant in small room air conditioning (AC) units, specifically window AC, packaged terminal AC/heat pumps (PTAC/PTHP), and mini-split heat pumps. Overall climate impact for a transition to all three types of air conditioning units in the 2022–2051 timeframe is found to be from 15 to 64 million metric tons of greenhouse gas (GHG) savings in California with a cost of saved CO2eq that ranges from $14.50 per ton of CO2eq saved to −$50.30 per ton of CO2eq saved (net savings) depending on whether the baseline refrigerant is R32 or R410A and depending on the relative energy efficiency for R290 units compared to baseline units.