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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]

• Developing countries face the dual challenges of industrialising and 'catching up' while mitigating CO 2 emissions. • Medium- and high-technology manufacturing is associated with lower emissions than low-technology manufacturing, with differences varying by the income levels of countries. • A shift to more technology-intensive manufacturing could be one aspect of environmentally sustainable industrialisation in developing countries. Industrialisation is an important engine of growth and 'catch up', but also associated with harmful carbon dioxide emissions and hence with climate change. This poses a challenge for sustainable industrial development, particularly for late industrialisers on how to industrialise while also mitigating carbon dioxide emissions. This paper investigates the effect of technology intensity across manufacturing industries on carbon dioxide emissions: are medium- and high-technology manufacturing industries less emissions-intensive than low-technology manufacturing industries in developing countries? The paper analyses this relationship for a panel of 68 developing and emerging economies over the period 1990–2016, by adapting the environmental Kuznets curve and the stochastic effect by regression on population, affluence and technology approaches. Using two alternative measures of emissions and estimating generalised method of moments model, the results show that medium- and high-technology manufacturing industries are associated with lower carbon dioxide emissions than low-technology manufacturing industries. The results also show that these differences vary by the income levels of countries. These findings have important policy implications, suggesting that a shift towards more technology-intensive manufacturing production processes may be a more environmentally sustainable industrialisation path for developing countries. [ABSTRACT FROM AUTHOR]

Schematic illustration of the double-layer PDRC coating. [Display omitted] • The idea of introducing masking layer for double-layer PDRC coating is proposed. • PDRC coating achieves high solar reflectivity (94.0%) at a thin thickness. • The PDRC coating reduces the indoor temperature of container room by 5.0 °C. • The prepared PDRC coating can realize large-scale long-time outdoor applications. The realization of spontaneous sub-ambient cooling in daytime typically requires the solar reflectance and thermal emittance of passive daytime radiative cooling (PDRC) material to be > 90%. Toward real-world applications, however, to achieve high optical performance and excellent durability, PDRC coating is often cost-prohibitive due to its high thickness (500 μm or higher). To solve the contradiction between high optical performance and thin thickness of PDRC coating, the idea of introducing masking layer for PDRC coating is proposed in this paper to prepare the water-based double-layer PDRC coating and explore its long-term outdoor application potential. The PDRC coating can achieve high solar reflectance (94.0%) and "atmospheric transparent spectrum window" emittance (93.0%), while the thickness and cost are only 40% and 60% of existing water-based PDRC coating. The PDRC coating shows efficient cooling performance when applied as the external coating of large temporary buildings, with maximum temperature reduction of 20.8 °C and 5.0 °C on the roof/indoor, which are equal to or greater than recorded values. The 150-day large-scale outdoor application shows that the coating has excellent durability, which offers a reference for the long-term outdoor application of PDRC coating. The application of the PDRC coating on rooftops of containers can achieve a cooling energy savings of up to 15.3% compared to conventional steel roofs, translating to an annual energy savings of 413.2 MJ and a reduction of approximately 114 kg in carbon dioxide emissions, which provide compelling evidence for the ability of PDRC coating to mitigate carbon emissions and address global climate change. [ABSTRACT FROM AUTHOR]

For making earlier realization on peak carbon dioxide emissions and carbon neutrality, hydropower development in countries all over the world can effectively reduce the Greenhouse Gas (GHG) emissions and solve the problem of global climate change. This paper proposes a novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index (OSGSVI) model to accurately predict the hydropower generation in some countries. Firstly, for enhancing the fitting accuracy, the initial condition is optimized based on the weighted average methods and the data grouping with OSVI is utilized by seasonal divisions. Secondly, an OSGSVI model is established coupled optimization on both optimized initial conditions and seasonal divisions. Thirdly, the Whale Optimized Algorithm (WOA) is employed to determine estimated parameters to further enhance the fitting accuracy for the hydropower generation. Finally, the experimental results of the prediction study show that three error measure values are all the smallest in all the fitting results and the MAPE values are converged before 30 iterations by utilizing our proposed model when compared with a set of baseline prediction models. It demonstrates the superiority of our proposed model over the others on the fitting accuracy with fast-convergence for the hydropower generation in these selected countries. • An OSGSVI model is proposed to accurately predict the hydropower generation. • The model uses the seasonal data grouping and OGSVI to enhance the predictive accuracy. • The WOA is proposed to further improve the predictive accuracy. [ABSTRACT FROM AUTHOR]

• Constructing a new welfare function considering economy, equity and ecology. • Optimizing carbon emission pathways from multiple perspectives. • Focusing on the emission pathways in China achieving the ambitious targets. With the increasing climate warming, reducing carbon emissions has gradually become a consensus around the world. However, the process of carbon emission reduction will inevitably lead to the transformation of economic structure, industrial structure and energy structure, which will bring huge changes in economic output, employment and other aspects, thus affecting the overall social welfare and equity. Therefore, it is particularly important for each country to determine a scientific and effective carbon emission pathway which take into account both social development and climate targets. As the largest carbon emitter, China has put forward the targets of carbon peak and carbon neutrality. In this regard, the corresponding carbon emission reduction scenarios are designed according to the new situation of China's emission reduction in this paper, firstly. Furthermore, under the premise of maximizing social welfare, an optimization model which integrating economic growth-equity-environment is developed to determine China's emission pathways under different scenarios from 2020 to 2050. Besides quantitatively showing the future total amount and peak value of carbon emissions under different scenarios, the results can also identify its evolution pathway over time. Therein, the peak under most scenarios will concentrate in 2025–2030, which is about 10.7 to 11.8 billion tons. With the increasingly stringent constraints on carbon emission reduction, the cost of emission reduction will increase correspondingly, but the overall social welfare will increase slightly. Finally, our findings provide practical guidance on achieving carbon emission reduction and enlightenments on policymaking. [ABSTRACT FROM AUTHOR]

As the first legally binding global agreement on climate change, the Paris Agreement aims to limit long-term global warming to less than 2 °C, preferably below 1.5 °C compared to pre-industrial levels. Road transport, as one of the primary sources of energy consumption and carbon emissions, has great potential for energy conservation and emission reduction in achieving the climate goals. However, current approaches cannot efficiently reduce road travel, making it challenging to decarbonize in the transport sector. Therefore, in this paper, we propose STRICTs, a blockchain-enabled motor vehicle restrictive and trading system based on the carbon emissions cap. STRICTs allows automated carbon emission auditing and carbon emission violation punishment without relying on third-party escrow. Through a decentralized enforcer, STRICTs also enables carbon permit trading reliably and transparently. Finally, we implement a proof-of-concept prototype of the system based on Hyperledger Fabric and conduct experiments for comprehensive performance evaluation through CO2 Emission by Vehicles dataset. The experiment results show the practically affordable performance of STRICTs. [Display omitted] • Carbon emission calculation and carbon emission permit allocation method is developed. • Vehicle data chain is built to guarantee the reliability and correctness of collected vehicle data. • Restrictions on carbon emission allowance is presented to reduce carbon emissions. • A smart-contract-based decentralized enforcer is proposed to audit user's carbon emission behavior. [ABSTRACT FROM AUTHOR]