Energy and climate change最新文献

Green hydrogen is expected to play a vital role in decarbonizing the energy system in Europe. However, large-scale deployment of green hydrogen has associated potential trade-offs in terms of climate and other environmental impacts. This study aims to shed light on a comprehensive sustainability assessment of this large-scale green hydrogen deployment based on the EMPIRE energy system modeling, compared with other decarbonization paths. Process-based Life Cycle Assessment (LCA) is applied and connected with the output of the energy system model, revealing 45% extra climate impact caused by the dedicated 50% extra renewable infrastructure to deliver green hydrogen for the demand in the sectors of industry and transport in Europe towards 2050. Whereas, the analysis shows that green hydrogen eventually wins on the climate impact within four designed scenarios (with green hydrogen, with blue hydrogen, without green hydrogen, and baseline), mainly compensated by its clean usage and renewable electricity supply. On the other hand, green hydrogen has a lower performance in other environmental impacts including human toxicity, ecotoxicity, mineral use, land use, and water depletion. Furthermore, a monetary valuation of Life Cycle Impact (LCI) is estimated to aggregate 13 categories of environmental impacts between different technologies. Results indicate that the total monetized LCI cost of green hydrogen production is relatively lower than that of blue hydrogen. In overview, a large-scale green hydrogen deployment potentially shifts the environmental pressure from climate and fossil resource use to human health, mineral resource use, and ecosystem damage due to its higher material consumption of the infrastructure.

Climate change is one of the most critical issues in the last decade, making investigating climate change risks' effects on the economy vital. Employing a novel time-varying Granger causality approach, we test causality from climate policy uncertainty (CPU) to the index returns of clean energy and nonrenewable energy sectors between November 2009 and December 2021. Our analysis generally reveals a significant causality from CPU to S&P clean energy sector index return throughout the sample period. In contrast, very limited significant causality is observed running from the CPU to the S&P nonrenewable energy index return. This result implies that investments in clean energy firms are affected by the CPU due to the cost of reversing the decisions in uncertain environments. On the other hand, the U.S. newspaper media coverage of climate change has a significant impact not only on the clean energy index returns but also on non-renewable energy index returns, implying an increase in the media coverage regarding climate change influences the awareness of investors on climate change, which affects their trading strategies.

As the availability of weather-dependent, zero marginal cost resources such as wind and solar power increases, a variety of flexible electricity loads, or ‘demand sinks’, could be deployed to use intermittently available low-cost electricity to produce valuable outputs. This study provides a general framework to evaluate any potential demand sink technology and understand its viability to be deployed cost-effectively in low-carbon power systems. We use an electricity system optimization model to assess 98 discrete combinations of capital costs and output values that collectively span the range of feasible characteristics of potential demand sink technologies. We find that candidates like hydrogen electrolysis, direct air capture, and flexible electric heating can all achieve significant installed capacity (>10% of system peak load) if lower capital costs are reached in the future. Demand sink technologies significantly increase installed wind and solar capacity while not significantly affecting battery storage, firm generating capacity, or the average cost of electricity.

Nuclear reactors and variable renewables will play a significant role in the global energy transition as providers of low carbon electricity to various end use sectors. Real time balancing of power demand and supply without modulation or curtailment is possible using electrolytic hydrogen plants and energy storage systems. The generation mix adopted and load profiles are unique to a country and this study considers the specific case of India. This work analyses the use of grid connected water electrolysers, grid scale battery storage, hydrogen storage and fuel cells as flexible loads and dispatch schemes for grid balancing. Based on postulated long term power generation scenarios for India, the minimum required system sizes for grid balancing are estimated and techno-economic uncertainties are assessed. The use of water electrolysers is prioritized to make use of excess power, while minimizing battery storage requirement. This scheme can potentially produce a substantial share of low carbon hydrogen in India for use in industrial decarbonization, thus reducing the need for additional generation infrastructure.

Europe has committed to turning climate neutral by 2050 while wider stakeholders acknowledge the need for a just low carbon transition that will alleviate any negative socio-economic impacts and leave no one behind. A key first step to this direction is to identify the regions at risk. We develop a dedicated socio-economic risk indicator which makes it possible to identify the EU regions likely to be affected the most from the transition. The indicator rests on the latest definition of the IPCC, which treats risk as the combination of Hazard, Exposure and Vulnerability. In our risk index, Hazard is described as the drop in production of fossil fuel-related sectors due to the transition risk, Exposure is the respective employment share, while Vulnerability is a composite index of socioeconomic sub-indicators that further describe Sensitivity and Adaptive Capacity of the regions. We find a wide divergence across the risk profiles of EU regions. 6 % of all EU regions are found to be at high risk, while 74 % of the regions face no risk. The 15 high-risk regions are also found to experience socioeconomic challenges prior to the low-carbon transition process, thus indicating the need for dedicated supporting policy mechanisms.

Japan has formulated a net-zero emissions target by 2050. Existing scenarios consistent with this target generally depend on carbon dioxide removal (CDR). In addition to domestic mitigation actions, the import of low-carbon energy carriers such as hydrogen and synfuels and negative emissions credits are alternative options for achieving net-zero emissions in Japan. Although the potential and costs of these actions depend on global energy system transition characteristics which can potentially be informed by the global integrated assessment models, they are not considered in current national scenario assessments. This study explores diverse options for achieving Japan's net-zero emissions target by 2050 using a national energy system model informed by international energy trade and emission credits costs estimated with a global energy system model. We found that demand-side electrification and approximately 100 Mt-CO₂ per year of CDR implementation, equivalent to approximately 10% of the current national CO₂ emissions, are essential across all net-zero emissions scenarios. Upscaling of domestically generated hydrogen-based alternative fuels and energy demand reduction can avoid further reliance on CDR. While imports of hydrogen-based energy carriers and emission credits are effective options, annual import costs exceed the current cost of fossil fuel imports. In addition, import dependency reaches approximately 50% in the scenario relying on hydrogen imports. This study highlights the importance of considering global trade when developing national net-zero emissions scenarios and describes potential new roles for global models.

Financial incentives, such as carbon credits and feed-in tariffs, are effective policy tools to mobilize renewable energy investment for combating climate change. However, climate and policy uncertainties also induce substantial financial risks to power companies’ investments. A company may view renewable energy as an opportunity or a risky business depending on its perception of how renewable technologies and energy policies evolve. To explore how the diverse response from individual companies affects the power system's adoption of renewables, this study develops an agent-based modeling framework that includes renewable technology advancement, market conditions, and changes in incentive programs in the agents’ decision-making. Power companies (i.e., agents) are assumed profit-driven and have different risk attitudes toward climate and energy policy uncertainty. For illustration, we applied the method to the Texas power system as a case study where a group of agents are randomly generated to represent the power companies’ aggregated behaviors. Agents’ risk attitudes are inferred based on a survey, historical data, and model diagnosis. Results of future scenarios highlight renewable adoption prediction uncertainties and the need to develop holistic modeling approaches to facilitate energy policy and power system planning. This modeling framework creates a flexible representation of the power industry and serves as a building block of our vision toward holistic power system modeling and planning. We discuss future research directions that extend the framework through model coupling for system reliability assessment and improve agent representation regarding risk perception and market dynamics.

We attribute variations in key energy sector indicators across global climate mitigation scenarios to climate ambition, assumptions in background socioeconomic scenarios, differences between models and an unattributed portion that depends on the interaction between these. The scenarios assessed have been generated by Integrated Assessment Models (IAMs) as part of a model intercomparison project exploring the Shared Socio-economic Pathways (SSPs) used by the climate science community. Climate ambition plays the most significant role in explaining many energy-related indicators, particularly those relevant to overall energy supply, the use of fossil fuels, final energy carriers and emissions. The role of socioeconomic background scenarios is more prominent for indicators influenced by population and GDP growth, such as those relating to final energy demand and nuclear energy. Variations across some indicators, including hydro, solar and wind generation, are largely attributable to inter-model differences. Our Shapley–Owen decomposition gives an unexplained residual not due to the average effects of the other factors, highlighting some indicators (such as the use of carbon capture and storage (CCS) for fossil fuels, or adopting hydrogen as an energy carrier) with outlier results for particular ambition-scenario-model combinations. This suggests guidance to policymakers on these indicators is the least robust.

Climate change poses a global challenge, and the electric power sector, as a major greenhouse gas contributor, plays a central role in tackling and curbing its effects. Despite significant research on global and national future pathways, there is a need for further exploration into the application of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) to understand sub-national impacts on the electric power sector. This study employs the Global Change Analysis Model (GCAM-USA) to analyze how climate change mitigation and socioeconomic development interact in the U.S. electric power sector at the state level. We developed four scenarios covering different levels of decarbonization efforts and socioeconomic development. Our research findings reveal a prevailing trend towards a less carbon-intensive U.S. electric sector, propelled by an expanding presence of natural gas and renewable energies in the energy mix. Such capital turnover leads to a significant reduction of overall CO₂ emissions from the electric sector, albeit at a higher lifetime cost in particularly eastern states. The mitigation efforts also lead to overall decreased water withdrawal and increased water consumption in the electric sector, however, disparities in state-level responses are observed. While population growth predominantly shapes electricity generation, unique state-level electrification potential yields indirect population-electricity dynamics. The spatially heterogeneous response suggests complex trade-offs associated with reconciling climate mitigation objectives with local electricity demand and resource constraints. In sum, this research equips policymakers and stakeholders with invaluable insights to formulate mitigation strategies that align with the objective of the U.S. electric sector, both at a national and international level, while also catering to the unique characteristics of each state.

This study assesses the role of geopolitical risk and uncertainty in the degradation of the environment by forming the functions for ecological footprint, CO₂ emissions, and load capacity factor for the period 1990–2019 in India. Besides, the specified function endogenizes economic growth, renewable energy consumption, and natural resource rent as the additional covariates. The use of the autoregressive distributed lag model (ARDL) confirms the long-run relationship between study variables. Further, the dynamic simulations of the autoregressive distributed lag model (DYNARDL) outcomes show that geopolitical risk improves the quality of the environment by reducing the ecological footprint and CO₂ emissions. However, it degrades the environment by reducing the load capacity factor. Furthermore, the uncertainty improves the environmental quality by reducing the CO₂ emissions and ecological footprint, but the reduced load capacity factor due to uncertainty implies the degradation of environmental quality in India. Given these findings, the study proposes different environmental conservation policies.