{"query_echo":{"q":"","filters":{"analysis_type":["decomposition analysis"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'decomposition analysis' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":6,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=decomposition+analysis","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"polint-000051","url":"https://climateinsightsdirectory.org/document/polint-000051","title":"Substituting the Future for the Past: A Decomposition Analysis Using MarketSim","publisher":{"name":"Institute for Policy Integrity","organization":"institute-for-policy-integrity"},"document_type":"research paper","language":"en","date_published":"2025-10-15","source_url":"https://policyintegrity.org/publications/detail/substituting-the-future-for-the-past-a-decomposition-analysis-using-marketsim","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","analysis_type"],"summary":{"text":"The research argues that government models for offshore oil and gas leasing often underestimate climate impacts by assuming continued fossil fuel reliance and ignoring climate initiatives. Using the Bureau of Ocean Energy Management’s Market Simulation Model, the authors demonstrate that accounting for increased substitution between fossil fuels and renewables leads to more elastic energy demand and significantly higher projected net climate effects from leasing policies.","ai_generated":true,"unit":"polint-000051#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Government models frequently underestimate the climate impacts of offshore leasing policies because they assume a continued reliance on fossil fuels and fail to account for ongoing climate initiatives.","ai_generated":true,"unit":"polint-000051#insight-1","date_modified":"2026-10-23"},{"text":"Theoretical analysis suggests that future climate action may increase the substitution between renewables and fossil fuels, which would result in more elastic energy demand.","ai_generated":true,"unit":"polint-000051#insight-2","date_modified":"2026-10-23"},{"text":"Integrating potential shifts in energy production and consumption into policy baselines leads to a significant increase in the projected net climate effects associated with offshore leasing.","ai_generated":true,"unit":"polint-000051#insight-3","date_modified":"2026-10-23"}],"climate_tags":["greenhouse gas emissions","renewable energy","energy demand"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["decomposition analysis","theoretical modeling"]},{"id":"sei-002151","url":"https://climateinsightsdirectory.org/document/sei-002151","title":"The 7th ASEAN Energy Outlook 2020 - 2050","publisher":{"name":"Stockholm Environment Institute","organization":"stockholm-environment-institute"},"document_type":"report","language":"en","date_published":"2022-10","source_url":"https://leap.sei.org/documents/AEO7.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The 7th ASEAN Energy Outlook (AEO7) analyzes energy demand, supply, and electricity generation across the ASEAN region through four scenarios: Baseline, AMS Targets (ATS), APAEC Targets (APS), and Least-Cost Optimisation (LCO). It finds that without policy intervention, energy demand will triple by 2050 and fossil fuels will continue to dominate. However, implementing national and regional policies can significantly reduce total final energy consumption and greenhouse gas emissions. The report emphasizes the need for increased energy efficiency, higher renewable energy penetration, and regional grid interconnection to ensure energy security and sustainability.","ai_generated":true,"unit":"sei-002151#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Under the Baseline Scenario, regional energy demand is projected to triple by 2050 compared to 2020 levels, with Total Final Energy Consumption (TFEC) reaching 1,281.7 Mtoe.","ai_generated":true,"unit":"sei-002151#insight-1","date_modified":"2026-10-23"},{"text":"Fossil fuels are expected to remain the dominant energy source in the Baseline Scenario, with oil accounting for 47.4% of TFEC, electricity for 20.3%, coal for 14.5%, and bioenergy for 9.2% by 2050.","ai_generated":true,"unit":"sei-002151#insight-2","date_modified":"2026-10-23"},{"text":"The region is projected to become a net importer of natural gas by 2025 and coal by 2039 if no significant new discoveries or infrastructure additions occur.","ai_generated":true,"unit":"sei-002151#insight-3","date_modified":"2026-10-23"},{"text":"Implementing regional policies in the APAEC Target Scenario (APS) could reduce TFEC by 53.7% by 2050 compared to the Baseline Scenario.","ai_generated":true,"unit":"sei-002151#insight-4","date_modified":"2026-10-23"},{"text":"ASEAN is projected to miss its regional target of 23% renewable energy (RE) in Total Primary Energy Supply (TPES) by 2025, with the AMS Target Scenario (ATS) predicting only 17.5%, leaving a 5.5% gap.","ai_generated":true,"unit":"sei-002151#insight-5","date_modified":"2026-10-23"},{"text":"The Least-Cost Optimisation (LCO) Scenario suggests a power system that costs USD 174.7 billion less than the APS over the 2021-2050 period, though it results in a 5.3% lower share of RE in TPES by 2050.","ai_generated":true,"unit":"sei-002151#insight-6","date_modified":"2026-10-23"},{"text":"Energy intensity reduction is projected to reach 29.2% by 2025 under ATS, which falls short of the 32% regional target set by APAEC.","ai_generated":true,"unit":"sei-002151#insight-7","date_modified":"2026-10-23"},{"text":"The APS scenario is projected to create up to 5.5 million new jobs by 2050, with 67% of these in hydropower and 10% in geothermal construction, operation, and maintenance.","ai_generated":true,"unit":"sei-002151#insight-8","date_modified":"2026-10-23"},{"text":"Biofuel production in the APS scenario would require approximately 5.6 million hectares of land for oil palm and 3.2 million hectares for sugar cane.","ai_generated":true,"unit":"sei-002151#insight-9","date_modified":"2026-10-23"},{"text":"The LCO Scenario identifies several cost-effective interconnection projects to be operational by 2025, including lines between Thailand and Peninsular Malaysia, Peninsular Malaysia and Sumatra, Sarawak and Brunei Darussalam, Lao PDR and Vietnam, Thailand and Myanmar, and Thailand and Cambodia.","ai_generated":true,"unit":"sei-002151#insight-10","date_modified":"2026-10-23"},{"text":"Universal electrification in ASEAN is projected to be achieved by 2040 under the ATS and APS scenarios, with Cambodia being the final country to reach this target.","ai_generated":true,"unit":"sei-002151#insight-11","date_modified":"2026-10-23"},{"text":"Clean cooking access is projected to reach only 90.28% by 2050 under the APS scenario, failing to meet the SDG7 target of 100% by 2030.","ai_generated":true,"unit":"sei-002151#insight-12","date_modified":"2026-10-23"},{"text":"The social cost of energy in 2050 is projected to be USD 5,457 billion in the Baseline Scenario, but could be reduced to USD 2,358 billion under the APS scenario.","ai_generated":true,"unit":"sei-002151#insight-13","date_modified":"2026-10-23"},{"text":"The LCO Scenario introduces nuclear energy into the region's power mix, with 5.2 GW deployed by 2050.","ai_generated":true,"unit":"sei-002151#insight-14","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","renewable energy","greenhouse gas emissions","energy security","energy transition","electric vehicle","biofuel","hydropower","geothermal energy","solar energy","wind power","nuclear power","carbon capture and storage","clean cooking","electrification","climate finance","energy storage"],"geography_discussed":["Brunei","Cambodia","Indonesia","Laos","Malaysia","Myanmar","Philippines","Singapore","Thailand","Vietnam"],"frame_perspective":[],"analysis_type":["scenario analysis","bottom-up modeling","least-cost optimisation","decomposition analysis"]},{"id":"clipolin-001092","url":"https://climateinsightsdirectory.org/document/clipolin-001092","title":"Energy and Misallocation: Evidence from Brazil","publisher":{"name":"Climate Policy Initiative","organization":"climate-policy-initiative"},"document_type":"research paper","language":"en","date_published":"2019-06","source_url":"https://www.climatepolicyinitiative.org/wp-content/uploads/2018/11/Energy-and-misallocation-evidence-from-brazil.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The research finds a positive correlation between energy and resource misallocation in Brazilian industry, suggesting that improving the allocation of resources to the most productive firms would simultaneously increase aggregate productivity and energy efficiency. Using data from 2007 to 2015, the authors demonstrate that resource misallocation increased over this period, acting as the primary barrier to productivity growth. While the potential aggregate output gains from fully equalizing revenue productivity are substantial, ranging from 77.9% to 96.2%, the analysis reveals that capital distortions are significantly more impactful than electricity distortions in generating these losses. The study concludes that broad industrial policies targeting these binding distortions are more effective for improving aggregate energy efficiency than firm-level initiatives.","ai_generated":true,"unit":"clipolin-001092#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Resource and energy misallocation in Brazil increased between 2007 and 2015, serving as the primary obstacle to the growth of aggregate productivity and energy efficiency.","ai_generated":true,"unit":"clipolin-001092#insight-1","date_modified":"2026-10-23"},{"text":"There is a positive cross-sectional relationship between energy allocation efficiency and overall resource allocation efficiency across 106 industries, indicating no trade-off between economic productivity and environmental gains.","ai_generated":true,"unit":"clipolin-001092#insight-2","date_modified":"2026-10-23"},{"text":"Full equalization of revenue productivity (TFPR) within industries could lead to aggregate manufacturing output gains between 77.9% and 96.2% for the period 2007-2015.","ai_generated":true,"unit":"clipolin-001092#insight-3","date_modified":"2026-10-23"},{"text":"Capital distortions are significantly more influential than electricity distortions in causing resource misallocation and productivity losses; eliminating capital distortions alone would increase productivity by 30.3%, while electricity distortions would only yield a 2.4% increase.","ai_generated":true,"unit":"clipolin-001092#insight-4","date_modified":"2026-10-23"},{"text":"The manufacturing of refined petroleum products exhibited the highest potential for output increase through resource reallocation at 290.4%, while the manufacture of musical instruments was the most efficient with a potential gain of 15.8%.","ai_generated":true,"unit":"clipolin-001092#insight-5","date_modified":"2026-10-23"},{"text":"Firm characteristics such as larger employment, higher human capital (skill level), and a higher share of family employees are positively associated with energy efficiency.","ai_generated":true,"unit":"clipolin-001092#insight-6","date_modified":"2026-10-23"},{"text":"The study suggests that industrial-level initiatives are more effective for improving energy efficiency in Brazil than firm-level programs, as the latter do not address the broader distortions that bind resource allocation.","ai_generated":true,"unit":"clipolin-001092#insight-7","date_modified":"2026-10-23"}],"climate_tags":["energy efficiency","industrial policy","electric energy consumption","industrial manufacturing","carbon dioxide emissions"],"geography_discussed":["People's Republic of China","India","United States","South Korea"],"frame_perspective":[],"analysis_type":["empirical research","economic modeling","decomposition analysis"]},{"id":"wri-001866","url":"https://climateinsightsdirectory.org/document/wri-001866","title":"Peaking China’s CO2 Emissions: Trends and Mitigation Potential","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"research paper","language":"en","date_published":"2015-09","source_url":"https://files.wri.org/d8/s3fs-public/uploads/WRI15_OCN_Peaking_Emissions_v4.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The paper analyzes reference and alternative scenarios for China's CO2 emissions, finding that while reference paths project emissions to double by 2050, alternative paths suggest a peak around 2030. It identifies GDP growth, energy intensity, and the energy mix as primary drivers, arguing that deep decarbonization through industrial restructuring and non-fossil fuel expansion is necessary to meet climate goals.","ai_generated":true,"unit":"wri-001866#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Reference scenarios, which include 'no new policy' and 'current policy' projections, indicate that China's CO2 emissions will continue to rise through 2030, with projections ranging from a 21 percent to a 119 percent increase relative to 2010 levels.","ai_generated":true,"unit":"wri-001866#insight-1","date_modified":"2026-10-23"},{"text":"Alternative scenarios, which assume more aggressive policies and breakthrough technologies, suggest a median emissions peak around 2030, with a median projected increase of 38 percent over 2010 levels by that year.","ai_generated":true,"unit":"wri-001866#insight-2","date_modified":"2026-10-23"},{"text":"The Deep Decarbonization Pathways Project (DDPP) scenario illustrates a path where energy-related CO2 emissions peak around 2030 at 11,477 Mt CO2 and then decline significantly to 5,173 Mt CO2 by 2050.","ai_generated":true,"unit":"wri-001866#insight-3","date_modified":"2026-10-23"},{"text":"Under the DDPP scenario, China's primary energy mix would shift from 71 percent coal in 2010 to 27 percent in 2050, while non-fossil fuels would increase from 7.9 percent to 42 percent.","ai_generated":true,"unit":"wri-001866#insight-4","date_modified":"2026-10-23"},{"text":"Carbon capture, utilization and storage (CCUS) is identified as a critical technology for deep decarbonization; the DDPP scenario assumes CCUS would capture 2,737 Mt CO2 by 2050, reducing total emissions by one third.","ai_generated":true,"unit":"wri-001866#insight-5","date_modified":"2026-10-23"},{"text":"GDP growth is the most significant source of uncertainty in emissions projections; a 1 percent increase in the annual average GDP growth rate (with energy intensity declining by the same amount) could increase 2020 CO2 emissions by 750 Million Tons.","ai_generated":true,"unit":"wri-001866#insight-6","date_modified":"2026-10-23"},{"text":"China's high energy intensity is attributed primarily to its economic structure, where industry accounts for over 40 percent of GDP and energy-intensive raw-materials industry consumes 50 percent of end-use energy.","ai_generated":true,"unit":"wri-001866#insight-7","date_modified":"2026-10-23"},{"text":"To achieve an emissions peak around 2030, the annual decline rate of CO2 intensity of GDP must be higher than the annual growth rate of GDP.","ai_generated":true,"unit":"wri-001866#insight-8","date_modified":"2026-10-23"},{"text":"The authors recommend that China shift its policy focus from carbon-intensity control to a 'dual control' system that targets both carbon intensity and total CO2 emissions.","ai_generated":true,"unit":"wri-001866#insight-9","date_modified":"2026-10-23"}],"climate_tags":["carbon dioxide emissions","emission intensity","coal","natural gas","nuclear power","solar energy","wind power","hydropower"],"geography_discussed":["European Union","United States","Japan"],"frame_perspective":[],"analysis_type":["scenario analysis","decomposition analysis"]},{"id":"bc3res-000142","url":"https://climateinsightsdirectory.org/document/bc3res-000142","title":"New climate scenario framework implementation in the GCAM integrated assessment model","publisher":{"name":"Basque Centre for Climate Change","organization":"basque-centre-for-climate-change"},"document_type":"research paper","language":"en","date_published":"2014-07","source_url":"https://www.bc3research.org/h6T8Uy8Nm4gFs0O/201408270933032121154692.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document assesses the impact of implementing the SSP2 scenario in the GCAM model, finding that a business-as-usual approach leads to a temperature increase of nearly 5ºC by 2095. It further evaluates a stabilization policy (RCP2.6) using a global carbon tax, which achieves negative emissions by 2100 through massive deployment of nuclear power, bioenergy with carbon capture and storage (BECCS), and afforestation, though it highlights tensions between economic efficiency and regional equity.","ai_generated":true,"unit":"bc3res-000142#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"In the GCAM-SSP2 baseline scenario, global temperatures are projected to surpass the 2ºC threshold by 2040 and increase to almost 5ºC by 2095, with CO2 concentrations exceeding 1155 ppm and radiative forcing reaching 9.5 W/m2.","ai_generated":true,"unit":"bc3res-000142#insight-1","date_modified":"2026-10-23"},{"text":"Under the RCP2.6 policy scenario, a global carbon tax starting in 2020 at 55 2011$/tCO2 and rising to 2,100 $/tCO2 by 2095 enables the stabilization of temperature below 1.5ºC relative to 1990.","ai_generated":true,"unit":"bc3res-000142#insight-2","date_modified":"2026-10-23"},{"text":"Achieving the RCP2.6 target requires a massive shift in the energy mix by 2095, with nuclear power providing over 50% of electricity and primary energy consumption consisting of 34% nuclear, 33% biomass, and 20% other renewables.","ai_generated":true,"unit":"bc3res-000142#insight-3","date_modified":"2026-10-23"},{"text":"The RCP2.6 policy achieves negative emissions by the end of the century through the large-scale deployment of biomass combined with Carbon Capture and Storage (CCS) and an initial massive afforestation effort of almost 1,000 Mha between 2020 and 2025.","ai_generated":true,"unit":"bc3res-000142#insight-4","date_modified":"2026-10-23"},{"text":"Mitigation costs for the RCP2.6 scenario are estimated to be below 2% of global GDP, but there is a regional disparity where emergent and in-development regions like the Former Soviet Union and Middle East face higher costs, exceeding 3% of GDP by 2095.","ai_generated":true,"unit":"bc3res-000142#insight-5","date_modified":"2026-10-23"},{"text":"The authors argue that GCAM's results may be overly optimistic regarding nuclear and fossil fuel costs, noting that the model uses outdated data that does not account for the 2011 Fukushima accident or the actual cost increases in nuclear power.","ai_generated":true,"unit":"bc3res-000142#insight-6","date_modified":"2026-10-23"},{"text":"The model's fossil fuel supply curves are identified as outdated, particularly for oil, where GCAM projects prices below $60 per barrel throughout the century, contradicting current market trends for unconventional fuels.","ai_generated":true,"unit":"bc3res-000142#insight-7","date_modified":"2026-10-23"}],"climate_tags":["carbon tax","nuclear power","bio-energy with carbon capture and storage","afforestation","emission intensity"],"geography_discussed":["Africa","People's Republic of China","India","United States","Japan","Western Europe","Former Soviet Union","Middle East","Latin America","Southeast Asia"],"frame_perspective":[],"analysis_type":["scenario analysis","decomposition analysis","policy evaluation"]},{"id":"climstra-000230","url":"https://climateinsightsdirectory.org/document/climstra-000230","title":"Leakage Risks in South Korea: Potential Impacts on Global Emissions","publisher":{"name":"Climate Strategies","organization":"climate-strategies"},"document_type":"policy brief","language":"en","date_published":"2013-11","source_url":"https://climatestrategies.org/wp-content/uploads/2016/01/cs-roy-et-al.-policy-paper-3-rev.-3-format.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The document evaluates the potential for carbon leakage in South Korea as it implements the Korean emission trading scheme (KETS). Using methodology from the EU ETS Directive, the analysis finds that several manufacturing sectors face a moderate to high risk of leakage, primarily driven by high trade intensity rather than additional production costs. The report highlights that while energy efficiency and fuel switching have historically reduced emissions intensity, the recovery of emission-intensive industries like steel and cement has increased structural risks. It recommends that the South Korean government consider mitigation policies such as free allowances, border adjustments, or import taxes to prevent the displacement of emissions to regions with less stringent climate policies.","ai_generated":true,"unit":"climstra-000230#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The Korean emission trading scheme (KETS), scheduled for launch in January 2015, is designed to cover approximately 70% of the country's greenhouse gas emissions, specifically targeting the power and industry sectors in its first three phases.","ai_generated":true,"unit":"climstra-000230#insight-1","date_modified":"2026-10-23"},{"text":"Decomposition analysis of CO2 emissions from 2000 to 2009 shows that for the overall economy, the output effect was the primary driver of emission increases, which was partially offset by a negative intensity effect from clean technologies and energy management.","ai_generated":true,"unit":"climstra-000230#insight-2","date_modified":"2026-10-23"},{"text":"In the manufacturing sector, a pronounced structural effect has increased CO2 emissions as the economy shifted back toward emission-intensive industries such as cement, iron, and steel following the 1997 financial crisis recovery.","ai_generated":true,"unit":"climstra-000230#insight-3","date_modified":"2026-10-23"},{"text":"The assessment identifies that eight manufacturing sectors are at potentially high risk of carbon leakage, with this risk being driven significantly by high trade intensity rather than additional production costs.","ai_generated":true,"unit":"climstra-000230#insight-4","date_modified":"2026-10-23"},{"text":"Specific manufacturing sectors identified as having a 'High Risk' of carbon leakage based on trade intensity (Criterion III) include chemicals and chemical products, textile and textile products, transport equipment, electrical and optical equipment, machinery, and leather and footwear.","ai_generated":true,"unit":"climstra-000230#insight-5","date_modified":"2026-10-23"},{"text":"Sectors such as 'Coke, refined petroleum and nuclear fuel' and 'Other non-metallic mineral' are classified as high risk across multiple criteria, including both additional production costs and trade intensity.","ai_generated":true,"unit":"climstra-000230#insight-6","date_modified":"2026-10-23"},{"text":"To mitigate leakage, the document suggests the South Korean government consider policies such as free allocation of allowances, border tax adjustments, output-based export rebates, or import taxes on CO2-intensive products from non-abating countries.","ai_generated":true,"unit":"climstra-000230#insight-7","date_modified":"2026-10-23"}],"climate_tags":["emission trading","carbon leakage","greenhouse gas emissions","carbon price","industrial manufacturing","energy efficiency"],"geography_discussed":["European Union","Japan","People's Republic of China","United States","Australia","New Zealand"],"frame_perspective":[],"analysis_type":["impact assessment","decomposition analysis","policy evaluation"]}]}