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Given the increasingly severe challenges of global climate change and sustainable development, accurate prediction of daily carbon dioxide (CO 2) emissions has become crucial. However, current research on real-time daily forecasts remains relatively scarce, often limited to annual predictions and point estimates. This paper proposes an innovative integrated model, based on quantile regression and Attention-Bidirectional Long Short-Term Memory (BILSTM), specifically designed for probabilistic prediction of daily carbon emissions. Testing the model on daily carbon emission data from major carbon-emitting countries including China, the United States, India, Russia, and Italy has demonstrated its superior performance across various metrics, including mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and normalized average width of prediction intervals (PINAW), compared to other benchmark models. This achievement not only highlights the model's effectiveness in predicting daily carbon emissions but also provides policymakers with a powerful tool for real-time monitoring and assessment of carbon emissions. Furthermore , the application of probabilistic forecasting methods enables decision-makers to gain a more comprehensive understanding of the potential range of future carbon emissions, providing strong support for the formulation of targeted policies. [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

Computationally inexpensive carbon cycle models serve as critical and efficient tools for illuminating the complex dynamics of the carbon cycle and its interplay with the climate system, offering insights into how our planet has responded to climate perturbations throughout its history. During geologic hyperthermal events, carbon cycle models are employed to trace the trajectory of carbon emissions and establish a connection between the emission trajectory and changes in the Earth's surface environment. To date, the prevalent method to estimate the carbon emission rate relies on coupling the carbon cycle modeling and proxy reconstructions. Most previous studies employ a forward methodology, i.e., they force the model with an array of predefined carbon injection scenarios and select the one that produces the best fit to one or more specific proxy-derived records (e.g., atmospheric pCO 2 , sea surface pH, and calcite compensation depth) as the optimal solution. However, this forward method has two potential disadvantages. First, it can be computationally expensive, particularly when tens of thousands of scenarios need to be conducted to find the best solution. Second, it might not yield the best injection trajectory if none of the predefined carbon emission curves represents the realistic emission curve. Hence, an inverse model that can reconstruct the carbon emission trajectory directly from the record/records is urgently needed. In this study, building upon the Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir (LOSCAR) Model (Zeebe, 2012), we develop an interactive carbon cycle model (named iLOSCAR) using the open-source Python language and include two options, a forward model and an inverse model. The forward model replicates the original LOSCAR model, while the inverse model calculates the emission trajectory constrained by the proxy records in a single run. Both models are accessible via a web-based interface, which allows users to interactively tune model parameters and conduct experiments. In this paper, we present the details of iLOSCAR, including model structure and derivation of key equations. We then validate iLOSCAR's performance through an identical twin test and model intercomparison. We also apply it to a climatic perturbation event to diagnose features of the emission pattern that were overlooked in previous studies. Finally, we discuss the possible directions for model's future development. • The iLOSCAR model, an extension of the classical LOSCAR model, is introduced. • The model is capable of replicating the forward mode from the original LOSCAR model. • The model can derive reliable reconstructions of carbon emission trajectories inversely from proxy records for the geologic hyperthermal events. • The model features an interactive web interface, enabling users to dynamically adjust parameters and conduct scenario-based experiments. [ABSTRACT FROM AUTHOR]

In 2022, Saudi Arabia's anthropogenic greenhouse gas emissions amounted to 810 million tons of CO 2 e. The country has pledged to cut its emissions by 278 million tons of CO 2 e per year by 2030. This paper contributes a modeling view on the climate-related effects of various energy policies on the Saudi energy system. We use an energy system model designed to represent seven sectors in the Saudi economy. In addition to a baseline, which entails the continuation of current domestic policies, we look at a scenario that incorporates some of the plans announced by the Saudi government. We also examine the baseline scenario with a cap on total CO 2 emissions equal to those exhibited with the announced plans. The model then makes the necessary decisions to abate CO 2. We find that the energy system contributes 130 million tons of CO 2 e to the nationally determined contribution amount in 2030. Also, a non-prescriptive scenario that caps energy-system CO 2 emissions produces the same emissions reductions as the announced plans while lowering the present cost by 2030 by $70 billion. The corresponding implicit marginal abatement cost of CO 2 approaches 35 $/tonCO 2 by 2045. This cost would displace around half of crude and fuel oil use in the Saudi energy system. • Saudi Arabia plans to cut annual greenhouse gas emissions by 278 million tonsCO 2 e. • We use a multi-sector model built from the ground up for Saudi Arabia. • The energy system can contribute 130 million tons of CO 2 e in reductions in 2030. • We find a scenario that achieves GHG abatement at a lower cost than official plans. • The marginal value attributed to CO 2 approaches 35 $ per tonCO 2 by 2045. [ABSTRACT FROM AUTHOR]

• A novel probabilistic method for evaluating the sustainability performance of reinforced concrete (RC) structures made with conventional and green concretes and subjected to carbonation is presented. • The method is based on the combination of life cycle assessment (LCA) and life cycle cost analysis (LCCA) methodologies. • The method takes into account the impact of climate change, which is illustrated for three locations with different types of climate. • It is shown that green concretes demonstrate superior sustainability performance compared to conventional concrete, both in terms of CO 2 emissions and costs, and that climate change does not affect that. The paper presents a probabilistic method based on two methodologies – Life Cycle Cost Analysis (LCCA) and Life Cycle Assessment (LCA), for evaluating the sustainability of reinforced concrete (RC) structures in terms of their costs and CO 2 emissions. The method considers the whole life of a RC structure by taking into account CO 2 initially embodied in its construction materials, the absorption of CO 2 by concrete due to carbonation during the service life of the structure, potential damage to the structure due to carbonation-induced corrosion of reinforcing steel that may require repairs, and relevant costs. Since there are numerous uncertainties associated with the calculation of CO 2 emissions and costs, a probabilistic approach is beneficial. The emphasis is made on RC structures made of the so-called "green concretes", in which Portland cement is partially replaced with supplementary cementitious materials such as fly ash and ground granulated blast-furnace slag. The issue of a changing climate is also addressed. The method is illustrated by assessing the sustainability of a multi-story RC carpark made of different concrete types at three different locations (London, Paris and Marseille) for present and future climate conditions. This assessment's results show that using green concretes leads to a major reduction in CO 2 emissions and a small decrease in the life-cycle cost of the carpark RC elements. The relative sustainability performance of green concretes slightly improves compared to Portland cement concrete for future climate conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the association between relative mobile broadband penetration (i.e. mobile broadband connections in total mobile connections) and carbon dioxide (CO 2) emissions globally. The study is based on 181 countries for the period 2002–2020. The results indicate an initial increase in CO 2 emissions for a country at an average emission level once mobile broadband is introduced. Possible explanations might be initial investment in network infrastructure and increased consumption of electricity. However, on average for the period 2002–2020 the continuous relationship between mobile broadband (defined as speeds of at least 256 kb/s) and CO 2 is significantly negative in a statistical sense, i.e. emissions at a country level significantly reduce as mobile broadband penetration increase. Based on a two-stage model and controlling for additional independent variables (i.e. GDP per capita, population density, the share of electricity consumption that comes from fossil fuel, industry as a share of GDP, a regulation index, fixed broadband penetration, working age population as a share of total population and a human capital index), we conclude that on average a 10 percentage points increase in relative mobile broadband penetration causes a 7 percent reduction of CO 2 emissions per capita (given that the instrumental variable strategy, as assumed, identifies causal effects). Thus, the results shows that investments in mobile infrastructure over longer periods of time can contribute to mitigating climate change. However, the relationship is only significant for high-income countries (i.e. countries with a GNI of $4096 or above). The results remain significant when mobile broadband is defined as mobile broadband connections per 100 inhabitants. • Initially, the introduction of MBB leads to increasing CO 2 -emissions. • Over time, increased MBB penetration leads to significantly reduced CO 2 -emissions. • A 10% increase in MBB penetration causes a 7% reduction in CO 2 -emissions per capita. • Investment in MBB over time help mitigating climate change in high-income countries. [ABSTRACT FROM AUTHOR]

Given the significant energy consumption and carbon emissions associated with buildings, there is increasing interest in improving sustainability of building operations to reduce impact on climate change. A common goal is to operate buildings as "net-zero" energy users in which all energy consumed by the building is balanced against renewable energy purchased from the grid or produced on site. To achieve net-zero status, many buildings will require significant retrofit so as to both reduce energy consumption in the absolute sense and provide the remainder without consuming fossil fuels. Thus, multi-year planning is required to ensure that goals can be met on time. In addition, due to the inherent uncertainty associated with energy consumption and generation, actually achieving net-zero energy use may require discretionary curtailment actions to be taken, and deciding whether such actions are necessary can be challenging. To address these challenges, we propose in this paper a design optimization and operational planning strategy to make the decisions needed to achieve sustainability goals in buildings. The strategy can be applied to schedule design changes over a long horizon to meet annual targets, and it can also be applied in closed loop on a shorter horizon to determine whether curtailment is needed to stay on track. We discuss the formulation of the optimization problem, solution methods, and modeling approaches for key parameters. Application of the strategy is illustrated via examples. Overall, this approach will help automate planning that is often done manually, allowing buildings to take a significant leap forward toward achieving their sustainability goals. • Achieving sustainability goals in buildings requires multi-year planning. • The required design and operational decisions can be made via optimization. • Key problem parameters can be obtained from data-driven modeling. • The strategy allows sustainability goals to be met at minimum cost. [ABSTRACT FROM AUTHOR]

• Forecasting-test framework for long-term power system planning. • Coal-fired generators can be completely withdrawn from the power system by 2060. • China's power capacity mix will transition to wind and solar generators dominated. • Biomass and gas-fired generators will take on the role of reserved generators. • The timing of achieving China's zero-coal power system may be advanced or delayed. Facing the challenges of climate deterioration, environmental pollution, and energy shortages, China has put forward the goals of "carbon dioxide peaking and carbon neutrality", and the electricity sector will be the key area and the main direction of carbon emissions reduction. To prospect China's power system and explore the potential for establishing China's zero-coal power system in the future, this paper proposes a forecasting-test framework for long-term power system planning. We predict, evaluate, and analyze China's power system from 2025 to 2060 using the proposed forecasting-test framework. During this process, the power capacity mix and the electricity generation mix of China's power system will gradually transition from coal-fired generators dominated to wind and solar generators dominated. Moreover, the roles of different generators and energy storages are illustrated through the seasonal comparison and sensitivity analysis of the installed capacity of generators. The results indicate that coal-fired generators can be completely withdrawn from the power system, confirming the feasibility and possibility of achieving China's zero-coal power system by 2060. Although the timing may be advanced or delayed in different scenarios, achieving China's zero-coal power system will require exercising nationwide planning and making joint efforts of various sectors from now on. [ABSTRACT FROM AUTHOR]

This study explores the impact of political uncertainty on sustainable urban development by examining carbon emission trading systems (ETS) in four major markets in China (Beijing, Shanghai, Guangdong, and Hubei) from 2014 to 2022. As an alternative to carbon taxes, carbon ETS markets have become increasingly popular due to their success in reducing greenhouse gas emissions. However, their effectiveness is often hindered by political instability and uncertainty. Utilising logistic regression and AR(1)-GARCH estimations, we identify a negative relationship between political uncertainty and carbon trading volume. Our study also reveals significant variations in the responses of these markets to political uncertainty. The paper contributes to the understanding of how ETS markets operate in a complex and constantly changing political environment. We suggest that policymakers need to consider the impact of political uncertainty on carbon trading when designing and implementing urban policies that promote sustainable development. Additionally, our research contributes to the development of urban policies that can be effectively implemented in both developed and developing regions. • Local government official turnovers are used as the measurement of political uncertainty. • Daily transaction data from four carbon ETS markets (Beijing, Shanghai, Hubei and Guangdong) in China • Considered the probability and proportion of zero transaction day • Logistic regression and AR(1)-GARCH estimations • A negative relationship between political uncertainty and carbon transaction volume [ABSTRACT FROM AUTHOR]

To mitigate climate change and achieve sustainable development, Chinese governments have introduced "1+N" "dual-carbon" policy system aimed at reducing CO 2 emissions, transforming energy structures and controlling pollution. An effective policy mix and synergy is crucial for achieving the emission reduction targets and for ensuring the effectiveness of the policies, and it is therefore of great practical importance to provide a pathway for policy synergy based on assessing policy effectiveness. This paper selects three policies, namely the carbon Emission Trading System (ETS), Coal Control and Clean Utilization (CCCU), and Green Financial Reform and Innovation Pilot Zone (GFPZ), sets up ten scenarios, adopts difference-in-differences (DID) models to evaluate the impact, and give a pathway for policy synergy. The results show that ETS, CCCU, and GFPZ policies have significant positive effects on carbon emission reduction, energy structure transformation, and industrial environmental pollution control, respectively; CCCU has synergistic effects with ETS and GFPZ policies in controlling coal consumption and increasing the completion of industrial pollution control investments, respectively; while GFPZ policies have conflicting effects with CCCU and ETS in reducing carbon emissions and coal consumption, the GFPZ policy has conflicting effects with CCCU and ETS in reducing carbon emissions and controlling coal consumption. Finally, the study proposes a phased policy support system around carbon emission reduction in the power generation industry, energy structure transformation, and industrial environmental pollution control before 2030 and from 2030 to 2060. [ABSTRACT FROM AUTHOR]