Energy and climate change最新文献

As decisionmakers at various scales begin to design strategies to implement the US net-zero goal, a holistic understanding of its broader economic and sustainability implications at subnational scales is important to shape public support and facilitate implementation. Here, we use an integrated assessment model to explore four different pathways toward the US net-zero goal and investigate their energy-water-land-health implications at the state level. We show that achieving the net-zero goal implies significant capital turnover (170–200 billion USD/year capital investment and 16–29 billion USD/year stranded assets in the power sector), reduced water withdrawal (120–210 km³/year), avoided air pollution damages (220–300 billion USD/year), and expanded forests (300–500 thousand km²). However, the economic and sustainability implications of achieving the net-zero goal at the state-level may not be correlated to a state's contribution to national emission reductions. Our study lays the foundations for a deeper understanding of the broader implications of the US net-zero goal to facilitate cost-effective and environmentally sustainable transitions toward that goal.

To quantify the impacts of China's climate mitigation policies on other nations, we use the multi-regional input-output model and the world input-output database (WIOD) 2014 to create several policy scenarios in this study. The results reveal that China's economic restructuring and emission intensity reduction policies reflect significant heterogeneity from both regional and sectoral perspectives. In absolute terms, developed countries have larger economic consequences and emission reductions than underdeveloped countries. In relative terms, the United States will lose only 0.1% of its GDP as China transitions to a services-driven economy by 2030, making it one of the least impacted countries. Countries like Australia and South Korea, which rely heavily on raw materials and China's traditional economic structure, would see their GDP shrink by 1.2–1.7% by 2030. It is worth noting that the central and eastern European countries have an obvious low-income - low-emissions agglomeration, and the potential for emission reductions in these countries needs to be further investigated. The most carbon-efficient industries are education and health care. Therefore, more investment in these relevant industries should be made in the coming decade to minimize the increase in carbon emissions associated with trade flows growth.

Backup generators (BG) are indispensable to address the potential electricity security risks caused by extreme weather, cyber attack and infrastructure failure. Diesel generators, the major BG component, are more polluting than grid electricity, which are in the blind spot of environmental regulation and statistics. To contribute to better environmental policies for the electricity sector, this study estimates the environmental impacts of backup generation in China. A BG capacity evaluation model is firstly developed based on the safety design rules of different buildings. Then, an environmental cost assessment model is constructed to quantify the impacts of pollutant emissions from BG after power outages. At last, this paper uses an improved Monte Carlo method to re-examine the estimated results considering multiple uncertain factors and analyzes their contributions to the total cost variance. Our major findings are: (1) The estimated total BG capacity is 169.46 GW in 2020, representing 7.70% of the total generation capacity in China. (2) The environmental cost of BG generation is 6.60 billion yuan in 2020. (3) Sichuan and Hubei suffer the highest environmental impacts at the provincial level.

Only a few studies on the overall impact of climate change on offshore wind power production and wind power ramps in the North Sea region have been published. This study focuses on the characteristics of expected wind power production and wind power ramps in the future climate aided by the classification of circulations patterns using a self-organizing map (SOM). A SOM is used to cluster high-resolution CMIP5-CORDEX sea level pressure data into 30 European area weather patterns. These patterns are used to better understand wind power production trends and any potential changes. An increased frequency of occurrence and extended persistence of high pressure systems lasting at least 24 h is projected in the future. Whereas a contrasting reducing tendency for low-pressure systems is estimated. No significant evidence is seen for a change in wind power capacity factor over the North Sea, though tentative evidence is seen for a reduction in wind power ramps. Annual energy production is seen to be dominated by a small number of weather patterns with westerly, south-westerly or north-westerly winds. Future wind power production is projected to become less from westerly winds and more from south-westerly and north-westerly flows. Ramp up events are primarily associated with strong south-westerly winds or weather patterns with a weak pressure gradient. Ramp down events have a stronger association with more north-westerly flow. In a future climate, a reduction in ramp up events associated with weak pressure gradients is projected.

In this study, machine learning (ML) was applied to investigate the suitability of a location to deploy five greenhouse gas removal (GGR) methods within a global context, based on a location's bio-geophysical and techno-economic characteristics. The GGR methods considered are forestation, enhanced weathering (EW), direct air carbon capture and storage (DACCS), bioenergy with carbon capture and storage (BECCS) and biochar. An unsupervised ML (hierarchical clustering) technique was applied to label the dataset. Seven supervised ML algorithms were applied in training and testing the labelled dataset with the k-Nearest neighbour (k-NN), Artificial Neural Network (ANN) and Random Forest algorithms having the highest performance accuracies of 96%, 98% and 100% respectively. A case study of Scotland's suitability to deploy these GGR methods was carried out with obtained results indicating a high correlation between the ML model results and information in the available literature. While the performance accuracy of the ML models was typically high (76 - 100%), an assessment of its decision-making logic (model interpretation) revealed some limitations regarding the impact of the various input variables on the outputs.

Energy projections are of great importance to energy policies but have consistently shown noticeable and repeated errors, and thus require accuracy assessment for improvement. International Energy Outlook (IEO) of the US Energy Information Administration and World Energy Outlook (WEO) of the International Energy Agency publish widely used energy projections, whose accuracy for China – the largest energy consuming economy and carbon dioxide emitter – has been rarely explored. This study investigates accuracy of China's reference energy projections in the annual reports of IEO and WEO from 2004 to 2019. Results show that most projections in IEO and WEO underestimated China's total energy consumption, particularly over longer projection horizons. The use of coal, natural gas and renewable energy tended to be underestimated, and nuclear energy was overestimated. The errors of industry and transport sectors were comparable and higher than for the other sectors. WEO showed substantially better accuracy than IEO in projections of total energy consumption, primary energy resources (except for nuclear energy) and end-use sectors. Projection horizon, errors in projected population's size, oil price and gross domestic product per capita were four leading factors related to the projection errors and hence they require particular attention in future modeling. For policy makers, this study shows that, if IEO and WEO projections are used to guide the policy making, China needs more aggressive policies in order to achieve the carbon neutrality goal.

While numerous studies have argued that public preferences need to be considered in energy transitions, the public has rarely informed the choice of normative scenarios from energy systems modelling. Using the case of Swiss electricity supply in 2035, we focus on a specific citizen group—school pupils of 10 to 18 years old—and use educational workshops to form and elicit their preferences in a way that helps choose scenarios from modelling. Initially, the 164 involved pupils had a simplistic understanding that future electricity supply mainly needs solar PV and electricity savings, but after our workshops they developed a more complete view that involved other technologies. The mean preferred scenario of the pupils for modelling relied on 88% renewable electricity, complemented by small shares of nuclear power, natural gas-based generation, and almost no net electricity imports. When compared to preferred scenarios for modelling elicited from 79 adult citizens and 60 energy experts in Switzerland, the pupils were less ambitious, since adults and experts preferred scenarios with 99% and 97% renewable sources on average. Most pupils opted for much more renewable electricity and less fossil fuel-based generation and imports than existing 82 model-based scenarios published in 2011–2018, indicating that the pupils’ preferred scenarios should complement existing scenarios.

Land-use conflicts may constrain the unprecedented rates of renewable energy deployment required to meet the decarbonization goals of the Inflation Reduction Act (IRA). This paper employs geospatially resolved data and a detailed electricity system capacity expansion model to generate 160 affordable, zero-carbon electricity supply portfolios for the American west and evaluates the land use impacts of each portfolio. Less than 4% of all sites suitable for solar development and 17% of all wind sites appear in this set of portfolios. Of these sites, 53% of solar and 85% of wind sites exhibit higher development risk and potential for land-related conflict. We thus find that clean electricity goals cannot be achieved affordably without substantial renewable development on sites with potential for land use conflict. However, this paper identifies significant flexibility across western U.S. states to site renewable energy or alter the composition of the electricity supply portfolio to ameliorate potential conflicts.

This study newly develops a recursive-dynamic global energy model with an hourly temporal resolution for electricity and hydrogen balances, aiming to assess the role of variable renewable energy (VRE) in a carbon-neutral world. This model, formulated as a large-scale linear programming model (with 500 million each of variables and constraints), calculates the energy supply for 100 regions by 2050. The detailed temporal resolution enables the model to incorporate the variable output of VRE and system integration options, such as batteries, water electrolysis, curtailment, and the flexible charging of battery electric vehicles. Optimization results suggest that combing various technical options suitable for local energy situations is critical to reducing global CO₂ emissions cost-effectively. Not only VRE but also CCS-equipped gas-fired and biomass-fired power plants largely contribute to decarbonizing power supply. The share of VRE in global power generation in 2050 is estimated to be 57% in a cost-effective case. The results also imply economic challenges for an energy system based on 100% renewable energy. For example, the average mitigation cost in 2050 is 69USD/tCO₂ in the cost-effective case, while it increases to 139USD/tCO₂ in the 100% renewable case. The robustness of this argument is tested by sensitivity analyses.

This paper investigates the energy mix and welfare implication of deep decarbonization pathways with net negative emission technologies for North America and globally to 2050 in a computable general equilibrium (CGE) framework. The analysis uses an integrated assessment model (IAM), the Global Change Assessment Model (GCAM), to develop three bounding emission scenarios: i) A business as usual pathway (BAU), ii) A pathway bounded by the Nationally Determined Contributions and attaining a 2°C end of century target (NDC-2°C), and iii) An increasing ambition pathway that attains a 1.5°C end of century target (NDC-1.5°C). The energy mix and economic impacts of these emissions pathways are then evaluated using Environment Canada's Multi-Sector, Multi-Regional (EC-MSMR) CGE model. When bioenergy with carbon capture and storage (BECCS) and direct air capture (DAC) are available, they play an important role in achieving emission reductions. Allowing the use of DAC preserves an additional 5% to 20% of the share of fossil fuels in North America. Including DAC in deep decarbonization pathways lowers the welfare loss by up to ∼1% globally compared to those without DAC in 2050. This finding is robust to both the estimated price of and constraints on DAC deployment. Increasing the potential for fuel switching in the CGE model further reduces the welfare effects for deep decarbonization.