Reduction Targets Research Articles

Corporate Engagement with the Science Based Targets Initiative: Financial Implications and Key Motivators

Objective: This study focuses on the Science Based Targets Initiative (SBTi), a voluntary framework that enables companies to set emission reduction targets based on science and aligned with the Paris Agreement to combat climate change in a credible and effective way. The research aims to examine the impact of companies’ participation in the SBTi on their stock performance, volatility, and financial risk. It also seeks to identify the key factors that motivate firms to participate in the SBTi. Methodology: The study collects and processes extensive data from SBTi and Refinitiv, utilizing statistical methods such as Difference-in-Differences (DiD) regression analysis and Probit models. It analyses financial variables, carbon emissions, as well as sector and geographic information of more than 4,000 companies, using panel data from 2015 to 2022. Results: The study finds a negative impact of SBTi on stock returns, while volatility and financial risk show no significant changes. Factors such as lower returns, reduced stock price fluctuation, and higher financial risk increase the likelihood of companies joining SBTi. Limitations: The study acknowledges certain limitations, including the focus on investor reactions to short-term targets and commitments. As more data become available, future research could explore a broader time horizon. Additionally, the study did not assess whether companies are meeting their targets. Lastly, a comparative analysis between companies from different regions and industries was not conducted, but is recommended for future research. Practical implications: The SBTi provides companies with flexibility in achieving their targets, which does not guarantee ambitious measures against climate change. Potential greenwashing practices may undermine investor confidence in the initiative’s standards. Firms must set realistic and challenging goals. Therefore, transparency and the integration of environmental commitments into business management are crucial for improving corporate perception and attracting capital, even though the financial benefits of these actions may not be immediate.

Assessing potential reductions of agricultural GHG in countries with different land productivities: Long-term integrated efficiency in DEA hybrid meta-frontier model.

Globally, the agricultural sector is responsible for the emission of ca. 9.3 gigatons of CO2 equivalent annually. A realistic efficiency model orientation, considering agricultural policy objectives in a given region of the world, is a crucial premise for finding the optimal path for achieving global targets of emission reduction. The main objective of this article is to assess agricultural greenhouse gases (GHG) potential reductions in countries with different farming productivity accounting for food security and economic performance of agricultural sector. The analysis focuses on non-radial slack that, theoretically, may be easily reduced by the improvement in available resource management. The DEA-based hybrid super-efficiency meta frontier model with undesirable output was employed, using the efficiency approach integrating three sustainability dimensions. The dataset consisted of data from 99 countries (2005-2018) divided into clusters. Several potential model orientations were tested and discussed with regard to agricultural policy objectives. It was found that by reducing slacks, agricultural emissions can be decreased by 0.74 Gt of CO2eq per year. Hence, removing only management inefficiencies would help achieve up to 80% of the global reduction targets in agriculture without a substantial technological change. However, the efficiency change component turned out to be mainly negative over the period studied; thus, a specific focus on agricultural policy is needed in terms of supporting farmers with a more rational use of their resources.

The REpeated ASSEssment of SurvivorS in intracerebral haemorrhage: protocol for a multicentre, prospective observational study

BackgroundThe REpeated ASSEssment of SurvivorS (REASSESS) study will conduct long-term cognitive, functional and neuropsychiatric performance assessments to determine whether evacuation of spontaneous intracerebral haemorrhage (ICH) reduces the risk of later cognitive decline in the ageing brain.Methods and analysisThis study will compare rates of cognitive decline under two treatment strategies for ICH. The first strategy is the use of minimally invasive surgery (MIS) with similar techniques as performed in (1) the Minimally Invasive Surgery plus rt-PA in the Treatment of Intracerebral haemorrhage Evacuation phase III (MISTIE III) trial, (2) the Early MiNimally-invasive Removal of IntraCerebral Haemorrhage (ENRICH) trial and (3) a single-centre cohort of consecutively treated patients with MIS. The second strategy is the current non-surgical standard of care using data from controls in MISTIE III and ENRICH and comparative data from The Ethnic/Racial Variations of ICH (ERICH) studyextended into the ERICH-Longitudinal study, which followed over 900 of ERICH cases with serial cognitive examinations. If successful, the REASSESS study could demonstrate that reduction of ICH volume is a critical target to reduce the risk of progressive cognitive decline, establish targets for residual haematoma volume reduction and determine if greater residual haematoma volume leads to a long-term inflammatory state.Ethics and disseminationApproval of this study was obtained from the Johns Hopkins University Institutional Review Board (IRB00311985). The findings of the study will be published in academic peer-reviewed journals.Trial registration numberNCT05611918; ClinicalTrials.gov; registered on 23 May 2023.

Human development welfare of renewable energy technology innovation under carbon reduction targets

ABSTRACT Renewable energy technology innovation (RETI) is widely recognised as a crucial solution to the energy crisis encountered during the human development process. However, there is a scarcity of studies that systematically investigate the impact of RETI on human development. Utilising panel data from 68 countries spanning the years 2006 to 2020, this study explores the effect of RETI on human development index (HDI) and reveals several important findings. First, RETI significantly promotes HDI. Second, RETI indirectly improves HDI through energy structure optimisation, energy efficiency improvement, and energy poverty alleviation. Third, RETI is more effective in improving HDI in countries with high quality of government governance, as well as in international energy agency (IEA) member countries. This research provides a novel perspective on HDI improvement from the perspective of RETI for the first time to the best of our knowledge.

Evidence on local climate policies achieving emission reduction targets by 2030

Evidence on local climate policies achieving emission reduction targets by 2030

Implementasi Kebijakan Penurunan Stunting di Kota Sukabumi

Stunting is a serious health problem that has a direct impact on the quality of human resources. Sukabumi City is one of the areas that has not been free from the problem of stunting. With a fairly high prevalence rate, the Sukabumi City Government is committed to reducing the prevalence of stunting through various policies and programs that have been and are being implemented. This article aims to analyze the implementation of policies to reduce the prevalence of stunting in Sukabumi City. The results show that the Sukabumi City Government has collaborated with various institutions from the health, education and social sectors that are integrated in reducing the prevalence of stunting. This collaboration has created synergy between SKPD and the pentahelix elements within it, resulting in several program innovations both at the city and regional levels. In addition, it is supported by the availability of resources including human resources, budget resources, health facilities, and information systems that can support the implementation of the program optimally. From the various programs that have been rolled out, it has shown a good commitment from the policy implementers, but efforts to motivate and increase active participation from the community continue to be made. The Regional Government through the Sukabumi City Health Office in implementing the stunting prevalence reduction program, continues to be committed and motivated to reduce the stunting prevalence rate with the hope of achieving the specified prevalence reduction target.

Artificial Intelligence (AI)-driven approach to climate action and sustainable development

Countries have pledged commitment to the 2030 Sustainable Development Goal (SDGs) and the Paris Agreement to combat climate change. To maximize synergies between SDGs and climate actions (CAs), we evaluate the alignment of national commitment to SDGs and emissions reduction targets by comparing action plans embodied in Voluntary National Review (VNR) reports and the Nationally Determined Contributions (NDCs) across 67 countries. An Artificial Intelligence (AI)-based approach is proposed in this study to explore the interconnectedness by applying machine learning classifier and natural language processing. Middle- and low-income countries with high emissions tend to have low NDC targets and contain similar information in VNR reports. High-income countries show less alignment between their NDCs and VNRs. The economic status of countries is found to be connected to their climate actions and SDGs alignment. Here, we demonstrate utility and promise in using AI techniques to unravel interactions between CA and SDG.

Environmental penalties and corporate carbon disclosure in China: divergent effects of resource availability and the role of social media pressure

IntroductionEnvironmental concerns and the escalating impacts of climate change have prompted governments to set ambitious carbon emission reduction targets. Corporations, as major contributors to carbon emissions, play a critical role in achieving these goals through transparent carbon disclosure. This study investigates the impact of environmental penalties on corporate carbon disclosure practices among Chinese-listed firms from 2009 to 2019, drawing on organizational legitimacy theory.MethodUsing data from publicly available corporate reports, we employed multivariate regression analysis to examine how environmental penalties influence carbon disclosure. Additionally, we explored the moderating roles of firms’ financial resource conditions and social media scrutiny.ResultThe results revealed that environmental penalties significantly enhance carbon disclosure, particularly in resource-redundant firms, while their effect diminishes in resource-constrained enterprises. Social media scrutiny amplifies the positive impact of penalties by increasing public pressure, highlighting the role of external stakeholders in fostering transparency.DiscussionThese findings underscore the importance of integrating tailored regulatory frameworks with active public and social media engagement to enhance corporate accountability and transparency. This study contributes to the literature on corporate social responsibility, offering actionable insights for policymakers, regulators, and corporate managers aiming to advance sustainability goals.

A hybrid model for carbon price forecasting based on SSA-NSTransformer: Considering the role of multi-stage carbon reduction targets.

In order to reduce the unpredictability of carbon prices caused by their increasingly prominent environmental and market attributes, and to minimize their negative impact on carbon trading, further research on forecasting models for carbon price is urgently needed. To improve the accuracy of prediction, this paper proposes a carbon price forecasting method based on SSA-NSTransformer. The method includes four main steps: Firstly, decomposition of carbon price signals, using Singular Spectrum Analysis to remove noise signals; Secondly, analysis of influencing factors, using Random Forest to identify and select key influencing factors of carbon price signal components from energy price, financial market, socio-economic, and environmental aspects; Furthermore, influencing factors prediction, considering the impact of different carbon reduction targets and predicting future trends of influencing factors; And finally, carbon price prediction, considering the impact of factors based on multi-stage carbon reduction targets, using Non-stationary Transformer to predict the signal components of carbon prices, reconstructing the carbon price time series, and testing the model accuracy. This paper uses historical data of ICE-EUA closing prices and influencing factors as samples, and the model error comparison results confirm that the proposed hybrid model for carbon price forecasting has significant advantages, which can provide scientific and accurate reference for carbon market participants' behaviors.

Digital government and carbon emissions: evidence from China

With the continuous modernization of national governance, the role of digital government construction in environmental protection and sustainable development has become increasingly prominent. This study explores the intrinsic link between digital government construction and carbon emissions and the mechanism of its influence based on 30 provincial-level panel data in China from 2017 to 2021. The study finds that digital government construction can significantly suppress carbon emissions, and this conclusion still holds after considering endogeneity issues and after multiple robustness tests. The study further reveals that digital government can inhibit carbon emissions through the internal mechanism of promoting industrial structure upgrading and green technology innovation. In addition, the inhibiting effect of digital government on carbon emissions is more pronounced in eastern and central provinces, provinces with higher levels of government transparency, higher levels of digital government development, and higher intensity of environmental regulation. This study provides an important reference for understanding the role of digital government construction in promoting green development and achieving carbon emission reduction targets.

Quantifying Socio-Regional Variability via Factor Analysis over China: Optimizing Residential Sector Emission Reduction Pathways

Policy synergy, the evidence-based coordination of public policies, can aid in more rapidly achieving air pollutant and carbon dioxide (CO2) emission reduction targets. Using logarithmic mean Divisia index (LMDI) decomposition, coupling coordination degree (CCD), and geographically and temporally weighted regression (GTWR) models, we analyzed the emission characteristics, drivers, and reduction pathways of residential air pollution across 30 Chinese provinces from 2001 to 2020. The southern provinces produced more air pollution than the northern provinces, with the gap widening after 2015. In the residential sector, energy emission factors (LMDI decomposition result, 686,681.9) and population size (14,331) had greater impacts on air pollutant emissions than the energy structure, energy intensity, synergies, or GDP per capita. The GTWR analysis of the CCD mechanism indicated that hydroelectricity and urbanization enhanced coupling coordination in the southeast. Meanwhile, in the west, coupling coordination was improved by R&D investment, government spending on industrial pollution control, electricity consumption, per capita cropland, temperature, and urbanization. This analysis provides a valuable reference for optimizing emission reduction strategies.

Effectiveness of agricultural BMPs on phosphorus load reduction for the Canadian Lake Erie basin: A literature review

Lake Erie, the shallowest and warmest of the Great Lakes, has been increasingly threatened by eutrophication and harmful algal blooms affecting economic growth in the region. Excess nutrients, particularly phosphorus, transported with agricultural runoffs have become a major issue of lake water quality. In response to this growing concern over the adverse environmental effects of agriculture, farmers, conservation authorities and governments have worked together to promote and implement best or beneficial management practices (BMPs) in the contributing watersheds that focus on maintaining agricultural activity and farm profitability while protecting the environment. This paper conducted a technical review of BMPs and their effectiveness on phosphorus load reduction from agricultural fields for the Canadian Lake Erie Basin (CLEB). These BMPs include cover crop, conservation tillage, manure incorporation, crop nutrient planning, fragile land retirement, adding organic amendments, reducing soil compaction, controlled tile drain, grassed waterway, vegetative filter strip, riparian buffer, water and sediment control basin, and wetland restoration. Field experiments and modeling studies about BMP effectiveness on phosphorus load reduction from agricultural fields in the CLEB and the Great Lakes region were reviewed and assessed. Findings from other similar geographic regions were also documented for comparison and reference. Because BMPs are site-specific, their phosphorus load reduction effectiveness varied over a wide range in the literature depending upon characteristics of the BMP and local climate, slope, soil type, land use, and land management conditions. Based on the literature review, factors influencing BMP effectiveness were analyzed, and suggestions on research priorities for evaluating BMP effectiveness in the CLEB were provided. This study will benefit decision-makers, watershed conservation authorities, and other stakeholders in estimating phosphorus load reductions from agricultural landscapes and contribute to tracking progress toward achieving Canada’s phosphorus load reduction targets for Lake Erie.

Exposure of insects to current use pesticide residues in soil and vegetation along spatial and temporal distribution in agricultural sites

Current use pesticides (CUPs) are recognised as the largest deliberate input of bioactive substances into terrestrial ecosystems and one of the main factors responsible for the current decline in insects in agricultural areas. To quantify seasonal insect exposure in the landscape at a regional scale (Rhineland-Palatine in Germany), we analysed the presence of multiple (93) active ingredients in CUPs across three different agricultural cultivation types (with each three fields: arable, vegetable, viticulture) and neighbouring meadows. We collected monthly soil and vegetation samples over a year. A total of 71 CUP residues in different mixtures was detected, with up to 28 CUPs in soil and 25 in vegetation in single samples. The concentrations and numbers of CUPs in vegetation fluctuated over the sampling period, peaking in the summer months in the vegetation but remaining almost constant in topsoil. We calculated in-field additive risks for earthworms, collembola, and soil-living wild bees using the measured soil concentrations of CUPs. Our results call for the need to assess CUP mixture risks at low concentrations, as multiple residues are chronically present in agricultural areas. Since this risk is not addressed in regulation, we emphasise the urgent need to implement global pesticide reduction targets.

From fossil fuels to alternative fuels: strategy development for a sustainable transport sector in Germany

BackgroundMany countries agreed to reduce CO2 emissions to limit global warming under the terms of the Paris Agreement. In Europe, this agreement is supported by the climate targets introduced under the European Green Deal and the Fit for 55 package. Although Germany has made substantial progress in reducing emissions across various sectors, the transport sector remains a notable exception, showing little improvement. It is therefore essential to reevaluate the transport sector to strengthen its contribution to achieving the emission reduction targets. The aim of this study is to identify and propose strategies for shifting from fossil fuel-based transport to a more sustainable mode centred on alternative fuels. To investigate the potential pathways, an integrated approach is developed using a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis and multi-criteria decision analysis (MCDA).ResultsA two-step survey was used to collect data from different stakeholders in order to derive the key factors for the implementation of alternative fuels and devise transition strategies. The findings show that reducing GHG emissions, resource competition, and the impacts of environmental regulations are the most important factors for evaluating the transition strategies. On the other hand, reducing the competitiveness of fossil fuels through increased prices, as well as technical and infrastructural support, are the most promising strategies.ConclusionsThe sustainable transition in the transport sector is fundamentally driven by the use of renewable fuel alternatives as sustainable energy carriers to replace fossil fuels. The use and deployment of renewable fuel alternatives will play the most significant role in the defossilization of the transport sector, on course to achieve a 55% reduction by 2030 and reaching climate-neutrality by 2050. However, identification of the proper transition strategies in the phase-out of fossil fuels and their replacement with renewable fuel alternatives necessitates a comprehensive evaluation framework. This work contributes to this by developing a holistic evaluation framework, enabling the incorporation of multiple stakeholders within the identification and evaluation of the transition strategies. While several strategies are identified, stakeholders agree that reducing the competitiveness of fossil fuels through increased prices and lower subsidies would be the best strategy.

Effectiveness of the Federal ‘Clean Air’ Project to Improve Air Quality in the Most Polluted Russian Cities

An unavoidable adverse consequence of industrial development is the contamination of urban atmospheres. Deterioration of air quality leads to a decrease in the quality of life of the population, creates a lot of risks of serious diseases, and threatens to increase life expectancy. This phenomenon is particularly evident in many large Russian cities, where historically a powerful industry has developed. In recent decades, the Russian government has acknowledged environmental remediation as a pivotal priority for the National Development Goals. The dedicated funding from the National ‘Ecology’ Project in 2018–2024 allowed for large-scale public and private investments to address the problem of improving the air quality of urban areas in Russia. What is the effectiveness of this spending? In this article, we answer this question by analyzing the effectiveness of the Federal ‘Clean Air’ Project, part of the National ‘Ecology’ Project, which aimed to improve air quality in 12 of the most polluted Russian cities. We show that the project’s key performance indicators (KPIs) underwent significant changes over the 2018–2024 period. The emissions reduction target was lowered from 22% to 20%, the methodology for measuring pollution was revised, and new targets were set. One of the main reasons for this was the suboptimal quality of the data on which the initial plan was based. As a result, the revised emissions estimates produced by the project were found to exceed not only the target benchmarks but also the baseline. The planned targets are largely on track, and it is likely that the target of a 20% reduction in emissions from the 2017 baseline will be met. However, the link between the KPIs and the improvement in urban air quality is questionable. The initial phase of the ‘Clean Air’ Project was a valuable first step, particularly in establishing an air quality monitoring network and conducting detailed pollution assessments in 12 cities. However, to further improve project performance, it is essential to base project KPIs on estimates of air pollution-related health damage and economic losses.

Optimizing Lightweight Material Selection in Automotive Engineering: A Hybrid Methodology Incorporating Ashby’s Method and VIKOR Analysis

The automotive industry is responsible for about 20% of greenhouse gas emissions in Europe, and it is under notable pressure to meet the reduction targets set by the European Union for the next decades. In this context, lightweighting is a very effective design strategy for which materials selection plays a key role. One of the main challenges of lightweighting is selecting materials with enhanced structural properties but a reduced weight in comparison with traditional solutions. The spectrum of available materials is very large, and the choice needs to be carefully evaluated based on multiple factors, such as mechanical behavior, raw materials cost, the availability of manufacturing processes, and environmental impact. This article presents an innovative methodology for materials selection in the lightweight automotive field based on the Ashby approach for mechanical performance coefficients as an initial filtering criterion. Following this preliminary screening, this study adopts the VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) MCDA (Multi-Criteria Decision Analysis) technique to rank feasible design solutions based on case study boundary conditions. The evaluation criterion of different design options encompasses crucial factors, such as mechanical properties, cost considerations, and environmental impact measures. The method is finally validated by the application of a redesign case study, a motor bracket of an electric commercial car.

Insights into the salt levels in bread offers in Slovenia: trends and differences.

Bakery products are considered as one of main dietary sources of sodium/salt in Slovenia. Our main objective was to assess the salt content in bread in Slovenia, focusing into different bread categories and sales channels. The data collected in 2022 was compared with year 2012. A follow-up study on salt content of bread sold in Slovenia was conducted. Bread samples were purchased in large retail shops and smaller bakeries across 11 statistical regions of Slovenia. Sodium content was determined by inductively coupled plasma mass spectrometry; salt content was calculated by multiplying sodium content with 2.54, assuming all sodium corresponds to sodium chloride. In 2022, 178 bread samples were purchased and analyzed. Weighted mean salt content in bread was 1.35 (95% CI 1.28-1.42) g/100 g in 2012, and 1.26 (95% CI 1.22-1.29) g/100 g in 2022, showing a 7% decrease. Notable differences in the salt content were observed between various bread subcategories and retail environments. In addition, a significant difference was observed between white wheat bread sold in large retail shops and smaller bakeries, where a higher salt content was observed. While study results show small decrease in the salt content in bread in Slovenia in last decade, the salt reduction targets set by the WHO have not been met. Additional efforts are needed to stimulate bread reformulation with reducing salt content.

Research on Battery Electric Vehicles’ DC Fast Charging Noise Emissions: Proposals to Reduce Environmental Noise Caused by Fast Charging Stations

The potential of electric vehicles (EVs) to support the decarbonization of the transportation sector, crucial for meeting greenhouse gas reduction targets under the Paris Agreement, is obvious. Despite their advantages, the adoption of electric vehicles faces limitations, particularly those related to battery range and charging times, which significantly impact the time needed for a trip compared to their combustion engine counterparts. However, recent improvements in fast charging technology have enhanced these aspects, making EVs more suitable for both daily and long-distance trips. EVs can now deal with long trips, with travel times only slightly longer than those of internal combustion engine (ICE) vehicles. Fast charging capabilities and infrastructure, such as 350 kW chargers, are essential for making EV travel times comparable to ICE vehicles, with brief stops every 2–3 h. Additionally, EVs help reduce noise pollution in urban areas, especially in noise-saturated environments, contributing to an overall decrease in urban sound levels. However, this research highlights a downside of DC (Direct Current) fast charging stations: high-frequency noise emissions during fast charging, which can disturb nearby residents, especially in urban and residential areas. This noise, a result of the growing fast charging infrastructure, has led to complaints and even operational restrictions for some charging stations. Noise-related disturbances are a significant urban issue. The World Health Organization identifies noise as a key contributor to health burdens in Europe, even when noise annoyance is subjective, influenced by individual factors like sensitivity, genetics, and lifestyle, as well as by the specific environment. This paper analyzes the sound emission of a broad sample of DC fast charging stations from leading EU market brands. The goal is to provide tools that assist manufacturers, installers, and operators of rapid charging stations in mitigating the aforementioned sound emissions in order to align these infrastructures with Sustainable Development Goals 3 and 11 adopted by all United Nations Member States in 2015.

An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models.

Human-induced global warming, primarily attributed to the rise in atmospheric CO2, poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO2 emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO2 emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO2 emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, India, the USA, and the EU27&UK). The 14 models used in the study include four statistical models (ARMA, ARIMA, SARMA, and SARIMA), three machine learning models (support vector machine (SVM), random forest (RF), and gradient boosting (GB)), and seven deep learning models (artificial neural network (ANN), recurrent neural network variations such as gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional-LSTM (BILSTM), and three hybrid combinations of CNN-RNN). Performance evaluation employs four metrics (R2, MAE, RMSE, and MAPE). The results show that the machine learning (ML) and deep learning (DL) models, with higher R2 (0.714-0.932) and lower RMSE (0.480-0.247) values, respectively, outperformed the statistical model, which had R2 (- 0.060-0.719) and RMSE (1.695-0.537) values, in predicting daily CO2 emissions across all four regions. The performance of the ML and DL models was further enhanced by differencing, a technique that improves accuracy by ensuring stationarity and creating additional features and patterns from which the model can learn. Additionally, applying ensemble techniques such as bagging and voting improved the performance of the ML models by approximately 9.6%, whereas hybrid combinations of CNN-RNN enhanced the performance of the RNN models. In summary, the performance of both the ML and DL models was relatively similar. However, due to the high computational requirements associated with DL models, the recommended models for daily CO2 emission prediction are ML models using the ensemble technique of voting and bagging. This model can assist in accurately forecasting daily emissions, aiding authorities in setting targets for CO2 emission reduction.

Evaluating the effectiveness of cost-minimal planning of decarbonized electricity systems in reducing life cycle greenhouse gas emissions

Abstract The decarbonization of regional electricity systems is a critical enabler of broader economy-wide decarbonization strategies and has motivated robust research on how to decarbonize electricity systems at minimum monetary cost. Since these efforts often focus on the operation of electricity systems, however, they do not account for emissions of the full life cycle associated with different resources. Different resource mixes can achieve a similar level of decarbonization apparent decarbonization through operational emissions, while achieving very different levels of life cycle greenhouse gas (GHG) emissions reductions. To explore these differences, we model the expansion of the California electricity system from 2030 to 2045 using a simplified electricity dispatch model to investigate how the planning of future electricity systems may differ between prioritizing minimum cost versus minimum life cycle GHG emissions under a common target for operational GHG reductions. We find that explicitly planning for minimum life cycle GHG emissions yields an additional 1.6-2.0% reduction in annual life cycle GHG emissions at a cost penalty of 3.2–9.6% and that electricity resource mixes that minimize life cycle GHG emissions tend to favor high capacity factor zero-carbon resources and long-duration storage compared to a minimum cost approach. Comparatively, we find that aggressive supply chain decarbonization of generation and storage technologies reduces life cycle GHG emissions of electricity supplies by 3.0% to 14% and combining both increases these reductions to 5.0% to 16%. Further, we find that applying a carbon tax to minimum cost-based capacity expansion can incentivize planning to account for life cycle GHG emissions. Our results indicate that a planning approach focused on minimizing life cycle GHG emissions may not be palatable, but significant reductions in life cycle emissions can be realized through conventional mechanisms like carbon taxes, import standards, and targeted supply chain decarbonization.