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Achieving low-carbon development of the cement industry in the developing countries is fundamental to global emissions abatement, considering the local construction industry's rapid growth. However, there is currently a lack of systematic and accurate accounting and projection of cement emissions in developing countries, which are characterized with lower basic economic country condition. Here, we provide bottom-up quantifications of emissions from global cement production and reveal a regional shift in the main contributors to global cement CO2 emissions. The study further explores cement emissions over 2020-2050 that correspond to different housing and infrastructure conditions and emissions mitigation options for all developing countries except China. We find that cement emissions in developing countries except China will reach 1.4-3.8 Gt in 2050 (depending on different industrialization trajectories), compared to their annual emissions of 0.7 Gt in 2018. The optimal combination of low-carbon measures could contribute to reducing annual emissions by around 65% in 2050 and cumulative emissions by around 48% over 2020-2050. The efficient technological paths towards a low carbon future of cement industry vary among the countries and infrastructure scenarios. Our results are essential to understanding future emissions patterns of the cement industry in the developing countries and can inform policies in the cement sector that contribute to meeting the climate targets set out in the Paris Agreement. The rapid deployment of low-carbon measures is urgently needed to reduce cement emissions as cement CO2 emissions from developing countries will almost deplete the remaining cement emissions budget within climate targets. [ABSTRACT FROM AUTHOR]

Abstract: China''s energy consumption doubled within the first 25 years of economic reforms initiated at the end of the 1970s, and doubled again in the past 5 years. It has resulted of a threefold CO2 emissions increase since early of 1980s. China''s heavy reliance on coal will make it the largest emitter of CO2 in the world. By combining structural decomposition and input–output analysis we seek to assess the driving forces of China''s CO2 emissions from 1980 to 2030. In our reference scenario, production-related CO2 emissions will increase another three times by 2030. Household consumption, capital investment and growth in exports will largely drive the increase in CO2 emissions. Efficiency gains will be partially offset the projected increases in consumption, but our scenarios show that this will not be sufficient if China''s consumption patterns converge to current US levels. Relying on efficiency improvements alone will not stabilize China''s future emissions. Our scenarios show that even extremely optimistic assumptions of widespread installation of carbon dioxide capture and storage will only slow the increase in CO2 emissions. [Copyright &y& Elsevier]

• A System Dynamics model is developed to characterise building stock turnover. • Five survival models are used to represent Chinese building lifetime distribution. • Bayesian Model Averaging is used to quantify both parameter and model uncertainties. • Posterior predictive distributions of stock and age-specific substocks are obtained. • Chinese urban residential building stock is projected to peak around 2065. Knowing the size of building stock is perhaps the most basic determinant in assessing energy use in buildings. However, official statistics on urban residential stock for many countries are piecemeal at best. Previous studies estimating stock size and energy use make various debateable methodological assumptions and only produce deterministic results. This paper presents a Bayesian approach to characterise stock turnover dynamics and estimate stock size uncertainties, applied here to China. Firstly, a probabilistic dynamic building stock turnover model is developed to describe the building aging and demolition process, governed by a hazard function specified by a parametric survival model. Secondly, using five candidate parametric survival models, the building stock turnover model is simulated through Markov Chain Monte Carlo to obtain posterior distributions of model-specific parameters, estimate marginal likelihood, and make predictions of stock size. Thirdly, Bayesian Model Averaging is applied to create a model ensemble that combines model-specific posterior predictive distributions of the recent historical stock evolution pathway in proportion to posterior model probabilities. Finally, the Bayesian Model Averaging model ensemble is extended to forecast future trajectories of residential stock development through 2100. The modelling results suggest that the total stock in China will peak around 2065, at between 42.4 and 50.1 billion m2. This Bayesian modelling framework produces probability distributions of annual total stock, age-specific substocks, annual new buildings and annual demolition rates. This can support future analysis of policy trade-offs across embodied-versus-operational energy consumption, in the context of sector-wide decarbonisation. [ABSTRACT FROM AUTHOR]