Have those countries declaring “zero carbon” or “carbon neutral” climate goals achieved carbon emissions-economic growth decoupling?

Zhao, Yihang; Su, Qun; Li, Bingkang; Zhang, Yuanyuan; Wang, Xuejie; Zhao, Huiru; Guo, Sen

doi:10.1016/j.jclepro.2022.132450

Cited by 89 publications

(27 citation statements)

References 38 publications

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“…Thus, by now, the economic perspective of studying the environmental inequality of countries has developed and prevails (Adom and Amoani, 2021;Duan et al, 2022;Sahin and Ayyildiz, 2022). Decarbonization carried out in support of sustainable development in the current interpretation is considered a factor that limits (slows down) economic growth (Sisodia et al, 2020;Zhao et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

AI’s contribution to combating climate change and achieving environmental justice in the global economy

Osipov

Skryl

2022

Front. Environ. Sci.

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Section: Literature Reviewmentioning

confidence: 99%

AI’s contribution to combating climate change and achieving environmental justice in the global economy

Osipov

Skryl

2022

Front. Environ. Sci.

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“…Moreover, the greenhouse effect caused by excessive CO 2 emissions contributes to the climate crisis, sea level rise, and ocean acidity increase [ 3 , 4 , 5 ]. Up to now, “zero carbon” or “carbon neutral” climate goals have been set up in more than 130 countries or regions [ 6 ]. In order to achieve this goal, research work focuses on finding clean energy alternatives to fossil fuels to reduce CO 2 emissions, and developing technologies such as CO 2 capture, storage, utilization, and chemical conversion to reduce the concentration of CO 2 in the air [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Porous Polymer Materials for CO2 Capture and Electrocatalytic Reduction

Wang

et al. 2023

Materials

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Efficient capture of CO2 and its conversion into other high value-added compounds by electrochemical methods is an effective way to reduce excess CO2 in the atmosphere. Porous polymeric materials hold great promise for selective adsorption and electrocatalytic reduction of CO2 due to their high specific surface area, tunable porosity, structural diversity, and chemical stability. Here, we review recent research advances in this field, including design of porous organic polymers (POPs), porous coordination polymers (PCPs), covalent organic frameworks (COFs), and functional nitrogen-containing polymers for capture and electrocatalytic reduction of CO2. In addition, key issues and prospects for the optimal design of porous polymers for future development are elucidated. This review is expected to shed new light on the development of advanced porous polymer electrocatalysts for efficient CO2 reduction.

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“…It is difficult for annual prediction to forecast small probability events affecting carbon emission based on historical data, while short-term carbon emission prediction can provide accurate prediction results based on recent data. It can provide scientific data support for policymakers to formulate carbon emission policies according to current socioeconomic conditions, thus better balancing the relationship between economic development and carbon emissions (Zhao et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy

Zhang

Ling

Zeng

et al. 2023

Environ Sci Pollut Res

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Accurate prediction of carbon emissions is vital to achieving carbon neutrality, which is one of the major goals of the global effort to protect the ecological environment. However, due to the high complexity and volatility of carbon emission time series, it is hard to forecast carbon emissions effectively. This research offers a novel decomposition-ensemble framework for multi-step prediction of short-term carbon emissions. The proposed framework involves three main steps: (i) data decomposition. A secondary decomposition method, which is a combination of empirical wavelet transform (EWT) and variational modal decomposition (VMD), is used to process the original data. (ii) Prediction and selection: ten models are used to forecast the processed data. Then, neighborhood mutual information (NMI) is used to select suitable sub-models from candidate models. (iii) Stacking ensemble: the stacking ensemble learning method is innovatively introduced to integrate the selected sub-models and output the final prediction results. For illustration and verification, the carbon emissions of three representative EU countries are used as our sample data. The empirical results show that the proposed framework is superior to other benchmark models in predictions 1, 15, and 30 steps ahead, with the mean absolute percentage error (MAPE) of the proposed framework being as low as 5.4475% in Italy dataset, 7.3159% in France dataset, and 8.6821% in Germany dataset.

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Have those countries declaring “zero carbon” or “carbon neutral” climate goals achieved carbon emissions-economic growth decoupling?

Cited by 89 publications

References 38 publications

AI’s contribution to combating climate change and achieving environmental justice in the global economy

AI’s contribution to combating climate change and achieving environmental justice in the global economy

Porous Polymer Materials for CO2 Capture and Electrocatalytic Reduction

Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy

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