Future Carbon Emissions Research Articles

Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model

Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here, we use a simple integrated assessment model, calibrated to century-scale data and expert assessments of baseline emissions, global economic growth, and population growth, to make probabilistic projections of carbon emissions through 2100. Under a variety of assumptions about fossil fuel resource levels and decarbonization rates, our projections largely agree with several emissions projections under current policy conditions. Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important. Our analysis also projects relatively low global economic growth rates over the remainder of the century. This illustrates the importance of additional research into economic growth dynamics for climate risk assessment, especially if pledged and future climate mitigation policies are weakened or have delayed implementations. These results showcase the power of using a simple, transparent, and calibrated model. While the simple model structure has several advantages, it also creates caveats for our results which are related to important areas for further research.

Estimation, analysis and comparison of carbon emissions and construction cost of the two tallest buildings located in United States and China

Nowadays, it is noticed that more than a third of the carbon dioxide (CO2) emitted in the atmosphere comes from the construction sector. This CO2 concentration has a significant effect on climate change. In the new cities, tall buildings multiply as mushrooms. Some specialists believe that they can be one of the solutions to reduce the carbon content in the atmosphere. The main aim of this study is to simulate, analyze and compare the embodied carbon and operational carbon of the two tallest buildings located in the United States and China, by using Design Builder and Pleiades software: the One World Trade Center in USA and the Shanghai Tower in China. Even if the embodied carbon of these super tall buildings is very high, the operational carbon remains the most important source of carbon emissions on their whole life cycle. Future carbon emissions of these two buildings were estimated in three periods (2030, 2050 and 2080) following the A2 scenario from the Intergovernmental Panel on Climate Change (IPCC). The results show that the operational carbon will increase by 10.6% at One World Trade Center (1WTC, USA) and 7.8% at Shanghai Tower (ST, China) in 2050. In addition, this study analyzed the impacts of the electricity mix and photovoltaic panels on their carbon dioxide emissions. Replacing energy production based on coal by renewable energy sources in the electricity mix of these countries could induce a reduction of 47.5% and 65.6% of total operational carbon emitted by the 1WTC and ST, respectively, by 2050. Finally, 46% of the building construction cost of these skyscrapers is related to their structure and superstructure.

Through the Looking Glass—The 2022 Edition: Trends in solid-state circuits from ISSCC

The International Solid-State Circuits Conference (ISSCC) is the flagship conference of the IEEE Solid-State Circuits Society. The theme for ISSCC 2022 is “Intelligent Silicon for a Sustainable World.” This theme further highlights the fact that to cope with strong environmental changes, the circuit design community is evolving more than ever to support a more sustainable world. Given their widespread use in our everyday lives, without change, ICs will become the dominant energy consumer and source of carbon emissions in the (near) future. Despite phenomenal advances in solid-state circuits and systems, low-power circuit design is becoming increasingly more sophisticated in all application areas, from digital computation and machine learning to analog design. In the context of data communications, advances in wireless and wireline circuits will help optimize local and global communications energy. Various sensors can be deployed to measure environmental data and optimize energy consumption. Power management techniques continue to be an essential aspect of a majority of circuits and systems to extend their lifetime and battery longevity. Furthermore, new technologies are providing opportunities to develop innovative circuits, reduce fabrication impacts, and improve recyclability.

Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing

The long-term impact of high-energy consumption in the manufacturing sector results in adverse environmental effects. Energy consumption and carbon emission prediction in the production environment is an essential requirement to mitigate climate change. The aim of this paper is to evaluate, model, construct, and validate the electricity generated data errors of an automotive component manufacturing company in South Africa for prediction of future transport manufacturing energy consumption and carbon emissions. The energy consumption and carbon emission data of an automotive component manufacturing company were explored for decision making, using data from 2016 to 2018 for prediction of future transport manufacturing energy consumption. The result is an ARIMA model with regression-correlated error fittings in the generalized least squares estimation of future forecast values for five years. The result is validated with RSS, showing an improvement of 89.61% in AR and 99.1% in MA when combined and an RMSE value of 449.8932 at a confidence level of 95%. This paper proposes a model for efficient prediction of energy consumption and carbon emissions for better decision making and utilize appropriate precautions to improve eco-friendly operation.

Lifecycle cost and carbon implications of residential solar-plus-storage in California

SummaryCapacities of residential photovoltaics (PV) and battery storage are rapidly growing, while their lifecycle cost and carbon implications are not well understood. Here, we integrate PV generation and load data for households in California to assess the current and future lifecycle cost and carbon emissions of solar-plus-storage systems. Our results show that installing PV reduces $180–$730 and 110–570 kgCO2 per year per household in 2020. However, compared to solar-only system, adding battery storage increases lifecycle costs by 39%–67%, while impact on emissions is mixed (−20% to 24%) depending on tariff structure and marginal emission factors. In 2040, under current decarbonization and cost trajectories, solar-plus-storage leads to up to 31% higher lifecycle costs and up to 32% higher emissions than solar-only systems. Designing a tariff structure with wider rate spreads aligned with marginal carbon emissions, and reducing the costs and embodied emissions of batteries are crucial for broader adoption of low-carbon residential solar-plus-storage.

Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

Uncovering Cleaner Method for Underground Metal Mining: Enterprise-Level Assessment for Current and Future Energy Consumption and Carbon Emission from Life-Cycle Perspective

China has committed to peak its carbon emissions by 2030, which puts forward a new issue for underground metal mines—selecting a cleaner mining method which requires less energy and generates less carbon emissions. This paper proposes an enterprise-level model to estimate life-cycle energy consumption and carbon emissions, which takes more carbon sources (e.g., cement and carbon sink loss) into consideration to provide more comprehensive insights. Moreover, this model is integrated with the energy-conservation supply curve and the carbon abatement cost curve to involve production capacity utilization in the prediction of future performance. These two approaches are applied to 30 underground iron mines. The results show that (1) caving-based cases have lower energy consumption and carbon emissions, i.e., 673.64 GJ/kt ore, 52.21 GJ/kt ore (only considering electricity and fossil fuel), and 12.11 CO2 eq/kt ore, as compared the backfilling-based cases, i.e., 710.08 GJ/kt ore, 63.70 GJ/kt ore, and 40.50 t CO2 eq/kt ore; (2) caving-based cases present higher carbon-abatement potential (more than 12.95%) than the backfilling-based vases (less than 9.68%); (3) improving capacity utilization facilitates unit cost reduction to mitigate energy consumption and carbon emissions, and the energy-conservation and carbon-abatement potentials will be developed accordingly.

Extensification and afforestation of cultivated mineral soil for climate change mitigation in Finland

Offsetting nation-wide CO2 emissions by carbon sinks from land use change (LUC), e.g. agricultural fields extensification and afforestation, is considered as a major climate change mitigation option. We evaluated the LUC potential for reducing emissions and creating annual soil and ecosystem carbon sinks in Finland. We used agricultural statistics, the forest growth model MOTTI, the soil carbon model Yasso07, and the RCP8.5 climate scenario.The soil carbon stock (SOC) of extensified grasslands showed on average less carbon loss than cropland, thus reducing future carbon emissions by LUC between 0.17 Mg ha−1 y-1, initially, and 0.08 Mg ha−1 y-1 after 50 years. The annual rate of such carbon gain was in comparison to SOC between 1.4‰ and 0.7‰ which is lower than proposed by the Paris 4‰ initiative for offsetting global anthropogenic CO2 emissions. Furthermore, after afforestation, estimated SOC is expected to increase above pre-LUC levels with 30 years lag. Estimated SOC sink from afforestation when compared to continuous cultivation varied depending on dominant tree species and soil fertility from between 0.19 Mg ha−1 y-1 (1.7‰ for spruce in medium fertile soil) to 0.46 Mg ha−1 y-1 (3.7‰ for silver birch in highly fertile soil). Future total soil and biomass carbon sink attributed to afforestation ranged between 1.65 and 2.44 Mg ha−1 y-1.Combined carbon sinks created by the present LUC could with 30 years lag offset annually between 0.01 and 4% of the present national net CO2 emissions in Finland. The long delay and a small scale of potential future carbon emission reduction by the LUC highlighted the importance of employing additional tools to reach the national neutrality targets due in next 15 or 30 years.

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.

(Invited) Mass Transport Considerations for High Performance CO2 Electrolysers

Electrocatalytic CO2 reduction has the dual-promise of neutralizing carbon emissions in the near future, while providing a long-term pathway to create energy-dense chemicals and fuels from atmospheric and waste CO2. The field has advanced immensely in recent years, taking significant strides towards commercial realization. Gas diffusion electrodes (GDEs), composed of solid electrocatalysts on porous supports positioned near the interface of a conducting electrolyte and CO2 gas, have been able to demonstrate the substantial current densities needed for future commercialization. These higher reaction rates have often been ascribed to the presence of a three-phase interface, where solid, liquid, and gas provide electrons, water, and CO2, respectively. Conversely, mechanistic work on electrochemical reactions implicate a fully two-phase reaction interface, where gas molecules reach the electrocatalyst’s surface by dissolution and diffusion through the electrolyte. Here, we first outline the macro, micro and atomistic phenomena occurring within a gas-diffusion electrode to provide a focused introduction to the architecture of the often-discussed three-phase region for CO2 electrolysis.In addition, we use a 2-D model to examine the concentration gradients in the gas and electrolyte flow channels across a GDE, which provides longitudinal information along the length of a flow cell. In doing so, we quantify the extent of concentration overpotentials and ohmic drops throughout the catalyst layer across a range of applied potentials and flow rates. Various process parameters were modified to explore the effects on the CO2 mass transfer-limited current density, conversion, and outlet concentrations of CO2, CO and H2. Our model suggests that ohmic losses largely determine the current density distributions at low conversion rates where small gradients in concentration exist. However, as the concentration profiles become less uniform (e.g. high conversion rates), the non-electrochemical consumption of CO2 starts to overtake electrochemical conversion, and leads to non-uniform performance across the electrode. In addition, while higher flow rates allow the ability to substantially increase current density, this comes at the expense of ohmic drops and losses in selectivity, which become more prominent at high conversion rates. The ability to understand the interplay between process conditions and local environment will be necessary to further develop the science and technology of electrochemical CO2 reduction.

Energy-related carbon emissions mitigation potential for the construction sector in China

China is undertaking a huge number of building and infrastructure projects. As a large consumer of energy-intensive building material, the construction activities provoke large direct carbon emissions in upstream industrial sectors (i.e. embodied carbon emissions). This paper aims to explore how construction-related climate policies could contribute to future national carbon emission mitigation efforts by employing a demand-side input-output model and scenario analysis. First, a hypothetical extraction approach is used to estimate the overall carbon emissions induced by the construction sector in the base year. Then scenario analysis is conducted to quantify the sector's technical potential for carbon mitigation in 2030 and 2050. We find that implementing construction-related climate measures in China could mitigate 2.5 Gt construction-induced CO2 in 2030, and 6.4 Gt in 2050 — more than Europe's annual total carbon emissions in 2015. More efficient electricity use could make a substantial contribution in the short-term. However, material-related initiatives, especially those focused on metal recycling, could yield significant carbon mitigation from 2030 onwards. Our findings suggest China to optimize the relationship between urbanization and construction to comply with the country's climate commitments better. Mechanisms to reform supply-side incentives, such as mandatory carbon labelling for construction material throughout the supply chain, could offer immediate benefits.

Future carbon emissions from global mangrove forest loss.

Mangroves have among the highest carbon densities of any tropical forest. These ‘blue carbon’ ecosystems can store large amounts of carbon for long periods, and their protection reduces greenhouse gas emissions and supports climate change mitigation. Incorporating mangroves into Nationally Determined Contributions to the Paris Agreement and their valuation on carbon markets requires predicting how the management of different land‐uses can prevent future greenhouse gas emissions and increase CO2 sequestration. We integrated comprehensive global datasets for carbon stocks, mangrove distribution, deforestation rates, and land‐use change drivers into a predictive model of mangrove carbon emissions. We project emissions and foregone soil carbon sequestration potential under ‘business as usual’ rates of mangrove loss. Emissions from mangrove loss could reach 2391 Tg CO2 eq by the end of the century, or 3392 Tg CO2 eq when considering foregone soil carbon sequestration. The highest emissions were predicted in southeast and south Asia (West Coral Triangle, Sunda Shelf, and the Bay of Bengal) due to conversion to aquaculture or agriculture, followed by the Caribbean (Tropical Northwest Atlantic) due to clearing and erosion, and the Andaman coast (West Myanmar) and north Brazil due to erosion. Together, these six regions accounted for 90% of the total potential CO2 eq future emissions. Mangrove loss has been slowing, and global emissions could be more than halved if reduced loss rates remain in the future. Notably, the location of global emission hotspots was consistent with every dataset used to calculate deforestation rates or with alternative assumptions about carbon storage and emissions. Our results indicate the regions in need of policy actions to address emissions arising from mangrove loss and the drivers that could be managed to prevent them.

Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature

Reliable and accurate environmental state prediction can help in long-term sustainable planning and management. Enormous land-use/ land-cover (LULC) transformation has been increasing the carbon emissions (CEs) and land surface temperature (LST) around the world. The study aimed to (i) examine the influences of land specific CEs on LST dynamics and (ii) simulate future potential LULC, CEs and LST pattern of Khulna City Corporation. Landsat satellite images of the year 2000, 2010 and 2020 were used to derive LULC, LST and CEs pattern and change. The correlation between land-use indices (NDBI, NDVI, NDWI) and LST was examined to explore the impacts of LULC change on LST. Unplanned urbanization has increased 11.79 Km2(26.10%) buildup areas and 25,268 tons of CEs during 2000–2020. The calculated R2 value indicates the strong positive correlation between CEs and LST. To simulate the future LULC, CEs and LST pattern for the year 2030 and 2040, multi-layer perceptron-Markov chain (MLP-MC)-based artificial neural network model was utilized with the accuracy rate of 94.12%, 99% and 98.48% for LULC, LST and CEs model, respectively. The simulation shows that by 2040, buildup area will increase to 87.33%, net CEs will increase by 19.82 × 104tons, and carbon absorptions will decrease by 23. 55 × 104tons and 69.54% of the total study area's LST will be above 390C. Such predictions signify the necessity of implementing a sustainable urban development plan immediately for the sustainable, habitable and sound urban environment.

Flexible Optimization of International Shipping Routes considering Carbon Emission Cost

Carbon emissions cost is a potential effective measure to restrict hydrocarbon pollution in the international shipping trade. The minimization of the total cost is pursued by ship operators, whereas voyage cost is increasingly involving the replacement of clean fuel and changing the cost of the shipping route. A flexible optimization method focusing on maximizing the total profit is developed in terms of sailing speed optimization and single port skips integrate carbon emission influence. An actual ocean shipping route from Shanghai to Rotterdam is applied to validate the effectiveness of the proposed models. The results have shown that the shipping route profit is volatile along with the sailing speed and the number of port calls. However, the profit will be maximized when applying the single port skip and will slow down the sailing speed at the same time. The demand of planned skip port can be supported by a short line container. A system composed of ocean container liner and short line container can improve the profit by 4.05% and reduce the carbon emission by 19.70%. Furthermore, sensitive results show that the profit is less affected by the changing of the carbon emission cost. A small size container has enough ability to solve the short transportation demand in adjacent ports and convert extraberthing cost into profit. These findings can provide reliable support for the shipping route decision process considering future carbon emission costs.

China's energy-related carbon emissions projections for the shared socioeconomic pathways

The carbon emissions from China's energy consumption are substantially increasing. In this context, it is necessary to predict the long-term dynamics of China's carbon emissions. Existing research has investigated future scenarios for China's carbon emissions, but there is still no consensus on such issues as the amount of emissions at peak points and the future carbon emissions path over a longer period. This paper aimed to explore the dynamics of China's carbon emissions under five Shared Socioeconomic Pathways scenarios (SSP1–SSP5), and to provide further evidences for the comprehensive analysis and prediction of climate change. Before forecasting the socioeconomic data, an in-sample and out-of-sample approach was used to evaluate the prediction accuracy of the feasible generalized least squares (FGLS) model. By using historical data from 30 provinces, the relationship among population, educational attainment, per capita GDP, and carbon emissions was investigated. Finally, carbon emissions from 2018 to 2100 were predicted based on the settings of different SSP scenarios and model parameters. The results showed that the peak value was 2030 for SSP1 and SSP5, 2029 for SSP2 and SSP4, and 2028 for SSP3. China will reach the largest cumulative carbon emissions amounting to 814.84 billion tons under the SSP5 scenario. Under all the SSP scenarios, the western region was always the first to reach its peak value, followed by the central region and then the eastern coastal zone. From 2018 to 2100, Jiangsu, Shandong, Guangdong, Zhejiang, Henan, Inner Mongolia, Xinjiang, Hebei, Hubei and Sichuan will contribute significantly to total carbon emissions under different SSP scenarios. All the results and conclusions would provide significant contributions for carbon reduction and climate change mitigation.

Re-calculation of responsibility distribution and spatiotemporal patterns of global production carbon emissions from the perspective of global value chain

This study proposes a non-differentiated producer responsibility (NDPR) principle to assign carbon dioxide emission responsibility. The NDPR principle redistributes carbon dioxide emissions along a value chain according to the proportion of profits obtained, which can eliminate the unfairness of carbon dioxide emissions transfers caused by transnational production processes. Compared with existing models, the NDPR model can be directly applied to a multi-region input–output table (MRIO), thereby improving the practical value of the method. Furthermore, this study applies the NDPR model to a long-term MRIO to analyze the spatiotemporal evolution of carbon emission responsibility worldwide. We found that both the volume and growth of carbon emissions are highly concentrated in European countries, the United States, and emerging market countries, and the basic emission pattern is stable. Both the producer and consumer responsibility principles lead to most developing countries in Asia bearing a greater proportion of the emission responsibility than the proportion of value they capture from global value chains. This study suggests that countries with the highest carbon emissions should assume the greatest responsibilities for global carbon emission reduction targets and develop measures and policies that significantly reduce carbon dioxide emissions. More attention should be paid to correcting the unfairness of carbon dioxide emission transfers within the global value chain and providing middle-income and low-income countries with more rights for development in future carbon emissions accounting.

Mandatory Corporate Carbon Disclosures and the Path to Net Zero

Despite widespread concern about global climate change, the overwhelming majority of publicly listed companies around the world still do not disclose their carbon emissions. Even fewer privately held companies do. Making carbon disclosures mandatory for both public and private companies is an elementary but essential step in the drive towards a net zero carbon economy. Firms should be required to report their annual direct greenhouse gas emissions, called Scope 1 emissions, as measured in CO2 equivalents, with possible deductions for high quality offsets. Going forward, firms should also be required to report the history of their annual carbon emissions. Such a disclosure mandate is simple, transparent, and readily implemented. It will aid policy makers and asset managers alike in managing the risks of carbon transition and accelerate the pace of future carbon emission reductions.

Sustainable Production–Inventory Model in Technical Cooperation on Investment to Reduce Carbon Emissions

Carbon cap-and-trade and carbon offsets are common and important carbon emission reduction policies in many countries. In addition, carbon emissions from business activities can be effectively reduced through specific capital investments in green technologies. Nevertheless, such capital investments are costly and not all enterprises can afford these investments. Therefore, if all members of a supply chain agree to share the investments in the facilities, the supply chain can reduce carbon emissions and generate more profit. Under carbon cap-and-trade and carbon tax policies, this study proposes a production–inventory model in which the buyer and vendor in the integrated supply chain agree to co-invest funds to reduce carbon emissions. We planned to integrate production, delivery, replenishment, and technology to reduce carbon emissions so as to maximize the total profit of the supply chain system. Several examples are simulated and the sensitivity analysis of the main parameters is carried out. The optimal solutions and joint total profit under various carbon emission policies are also compared. The future carbon emission control trend is expected to enable companies to share risks by co-investing and developing sustainable supply chains.

Will the urbanization process influence the peak of carbon emissions in the building sector? A dynamic scenario simulation

The accelerating urbanization has posed great challenges to China’s carbon-mitigation efforts and global climate change response. However, how does the urbanization process impact the peak of future carbon emissions from buildings is still unclear. This study first establishes a comprehensive urbanization framework from three dimensions. Then, a dynamic scenario simulation model is innovatively constructed by combining the extended Kaya identity and Monte Carlo simulation approach to explore future dynamic evolution trajectory, possible peaks and peaking time of China’s building carbon emissions from 2000 to 2050, considering the uncertainties of factors. Finally, dynamic sensitivity analysis is performed to investigate the contributions of different dimensions of the urbanization. Results show that under the business-as-usual scenario, the rural residential building sector will be the first to reach its energy peak in 2027. Dynamic scenario simulation shows that the building sector will peak at 3.09 (±0.36) Bt CO2 in 2037 (±4). Specifically, the urban residential building sector will peak at 1.27 (±0.18) Bt CO2 in 2040 (±4), while the rural residential building sector will peak at 0.51 (±0.03) Bt CO2 in 2021 (±4) and the commercial building sector will peak in 2038 (±4) at 1.41 (±0.30) Bt CO2. Dynamic sensitivity analysis indicates that rural–urban migration will exert promoting effect on the peaking time and emission peaks of building carbon emissions through the concomitant effects of the urbanization process, i.e., the economic urbanization and space urbanization. Overall, this study can provide a better insight into the impact of urbanization on carbon emission peak and can support governments and decision-makers to formulate effective carbon-mitigation strategies.

Chinese Overseas Development Financing of Electric Power Generation: A Comparative Analysis

Summary Global power generation must rapidly decarbonize by mid-century to meet the goal of stabilizing global warming below 2°C. To meet this objective, multilateral development banks (MDBs) have gradually reduced fossil fuel and increased renewable energy financing. Meanwhile, globally active national development finance institutions (DFIs) from Japan and South Korea have continued to finance overseas coal plants. Less is known about the increasingly active Chinese DFIs. Here, we construct a new dataset of China’s policy banks’ overseas power generation financing and compare their technology choices and impact on generation capacity with MDBs and Japanese and South Korean DFIs. We find that Chinese DFI power financing since 2000 has dramatically increased, surpassing other East Asian national DFIs and the major MDBs’ collective public sector power financing in 2013. As most Chinese DFI financing is currently in coal, decarbonization of their power investments will be critical in reducing future carbon emissions from recipient countries.