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Employment creation and climate change mitigation are core tasks for achieving sustainable development goals. Whether or not carbon mitigation policy facilitate employment deserves deep exploration. Through the construction of multi-regional dynamic computable general equilibrium model (CGE) with more scientific energy & environment block, this paper first evaluates regional employment effects of the national emission trading scheme (ETS) in China. Furthermore, we explore the Okun's law of the national ETS based on the mediating effect model. The results show that whether in carbon-intensive industries (CIIs) or non-carbon-intensive industries (NCIIs), employment effects of the national ETS are differentiated across regions. Specifically, the national ETS generally promotes CIIs' employment in Southern, Eastern, Middle Yangtze River and Southwest regions, and has negative effects on CIIs' employment in other regions. Meanwhile, the national ETS brings employment creation to NCIIs of Southern region, while there are opposite results in NCIIs of Northeast region and mixed results in NCIIs of other regions. Moreover, the Okun's law of the national ETS holds in CIIs of each region, but it not fits the data for NCIIs. Therefore, it is important for the Chinese government to consider the differentiated employment effects in different regions carefully rather than adopt one-size-fit-all solution when constructing the national carbon market. [Display omitted] • Multi-regional dynamic CGE model is constructed for investigate. • The carbon emission accounting method in this paper is more scientific. • This paper focuses on employment effects of the national ETS at regional level. • Almost all regions would unavoidably witness more or less employment losses. • The Okun's law of the national ETS does not always hold. [ABSTRACT FROM AUTHOR]

The cement industry plays a significant role in global carbon emissions, underscoring the urgent need for measures to transition it toward a net-zero carbon footprint. This paper presents a detailed plan to this end, examining the current state of the cement sector, its carbon output, and the imperative for emission reduction. It delves into various low-CO 2 technologies and emerging innovations such as alkali-activated cements, calcium looping, electrification, and bio-inspired materials. Economic and policy factors, including cost assessments and governmental regulations, are considered alongside challenges and potential solutions. Concluding with future prospects, the paper offers recommendations for policymakers, industry players, and researchers, highlighting the roadmap's critical role in achieving a carbon-neutral cement sector. • Cement industry faces urgent need for net-zero transition due to high carbon emissions. • Roadmap spans phases: foundation, acceleration, net-zero, targeting emissions by 2050. • Innovations like alkali-activated cements offer promise for emission reduction. • Collective action and policy crucial for sustainable transformation of cement sector. [ABSTRACT FROM AUTHOR]

This study delves into the strategies employed by small and medium-sized enterprises (SMEs) to embed digital technology into their business models, aiming to reach carbon neutrality. We offer a comprehensive analysis of five high technology SMEs, unveiling an evolutionary process model that highlights their journey toward integrating technology. The integration process is delineated into three progressive stages. Initially, digital technology is seen as an 'enabler' that promotes the birth of an innovative business model, essentially transforming the mechanisms of value creation, delivery, and capture. As SMEs evolve to an intermediate stage, digital technology becomes a 'disruptor', reshaping and reinventing the business model in terms of how value is created, delivered, and captured. Entering the maturity stage, digital technology is elevated to the role of 'expertise', leading to a sophisticated business model that refines and optimizes these aspects of value. The influence of these business model innovations on carbon neutrality is complex and varies distinctly with each stage, reflecting the dynamic interplay between digital technology and business model evolution. • This study explores the unique strategies SMEs implement to weave DT into their business model innovation (BMI) with an aim to achieve carbon neutrality. • The paper presents an evolutionary process model that sheds light on this integration journey, using an in-depth analysis of five high-tech SMEs. • The paper elaborates the relationship between DT and BMI, delving into the integration of DTs and the roles they perform - as enablers, disruptors, and areas of expertise. • The paper provides insights for businesses seeking to leverage digital technologies to drive innovation and growth. [ABSTRACT FROM AUTHOR]

The digital economy (DIE), a new economic form with digitalization at its core, has become an important driving force for promoting regional economy development. In the context of the COVID-19 pandemic, exploring the impact path of the DIE on carbon emission efficiency (CEE) is conducive to giving full play to the "carbon-reduction-and-efficiency-enhancement" role of the DIE, and to promoting the realization the "dual carbon" goal of carbon peak and carbon neutrality. In this paper, the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB) are taken as study areas, the panel Tobit model is used to explore the impact of the DIE on CEE, and the intermediary-effect model and threshold-effect model are constructed to test the intermediary and threshold effects of technological innovation, respectively. The results show that the DIE has a U-shaped nonlinear impact on CEE in both the YRB and the YREB and that the impact has regional heterogeneity. Technological innovation can play a mediating effect between the DIE and CEE, whereas the mediating effect in the YRB is stronger than that in the YREB. Technological innovation has a threshold effect on the DIE to improve CEE, while the threshold value in the YREB is higher than that in the YRB. Furthermore, this paper proposes some suggestions to guide regional low-carbon and sustainable development. Impact path of digital economy (DIE) on carbon emission efficiency (CEE) in the Yellow River Basin (YRB) and Yangtze River Economic Belt (YREB). [Display omitted] • The Super-SBM model is used to evaluate the urban carbon emission efficiency (CEE), and the entropy weight method is used to evaluate the development level of urban digital economy (DIE). • The DIE has a U-shaped nonlinear impact on CEE in the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB). • The intermediary model and threshold model are used to test the intermediary effect and threshold effect of technological innovation between DIE and CEE. • Technological innovation can play a mediating effect between DIE and CEE, whereas the mediating effect in the YRB is stronger than that in the YREB. [ABSTRACT FROM AUTHOR]

This paper interprets the implicit carbon flows in global industrial sectors from a network perspective. Using the SNA-IO integrated model, along with cross-border input-output data from Eora26 (2000–2020) and global energy balance data, the implicit carbon emissions of global industrial sectors and their evolution are analyzed. A carbon emission network structure from an industrial chain perspective is proposed. The results indicate that the carbon emissions responsibility of an industry is not only associated with its own energy consumption. It also involves the carbon emissions transfer resulting from the exchange of products and services between upstream and downstream industries. Block model analysis reveals the carbon emission transfer relationships and their interconnections among global industrial sectors, tending towards an industry clustering pattern where "production side" converges with "demand side" coexisting in supply and demand. There are noticeable inequalities in wealth gains and environmental burdens between these blocks. This paper can provide targeted carbon reduction policy recommendations for various industrial sectors to participate in global responsibility allocation and promote the formation of a low-carbon global industrial sector network. [Display omitted] • The implicit carbon flows in global industrial sectors are explored. • Carbon emission network structure from an industrial chain perspective is proposed. • Supply chains and logistics networks reduce implicit carbon emissions significantly. • Wealth gains and environmental burdens exist noticeable inequalities during blocks. [ABSTRACT FROM AUTHOR]

China's double carbon target aims to improve human well-being and sustainable development. Energy transformation welfare performance (ETWP) is the efficiency of energy transition (ET) in enhancing human well-being. ETWP considers both human well-being and sustainable development. Research on its driving force is helpful in achieving the double carbon goal. Thus, this paper used Logarithmic Mean Divisia Index Model, Fixed Panel Regression Model and Grey Relational Analysis Model to analyze China's ETWP from 2006 to 2022 and predicted ETWP of 31 provinces from 2023 to 2030. The results showed that: (1) ETWP had two rising periods in 2006–2014 and 2015–2022. (2) The government's rationalization policy on energy and environment and technological innovation ability were fundamental driving forces for improving ETWP. (3) There were obvious spatial and temporal distinctions in ETWP, and it would bring out different degrees in most areas. Thus, ET should be promoted by improving the ecological environment and resource utilization efficiency; The importance of the role of scientific and technological innovation and policies should be focused on in promoting ETWP; ET policies based on local developments should be formulated and the energy structure should be changed. Figure Geographical map of ETWP from 2014 to 2022. [Display omitted] • The concept of energy transition welfare performance was defined and accounted for. • The driving factors of energy transition welfare performance were assessed. • The spatial differences of energy transition welfare performance was studied. [ABSTRACT FROM AUTHOR]

Traditional manufacturing industry is in the early stages of transition to low-carbon innovative production, and is in urgent need of a low-carbon innovation system to achieve the goal of carbon neutrality. In order to realize the effective supervision of enterprise carbon emissions, this paper constructs a tripartite evolutionary game model among the corporate, government and public from the perspective of dynamic subsidies and taxes. The main results are as follows. First, the increase in government subsidies to a certain extent will help encourage companies to choose low-carbon innovative production strategies, but more subsidies are not always better. Excessive subsidies will increase the cost of government regulation and reduce the probability of government regulation. Second, the tripartite evolutionary game system does not converge under the static subsidies and taxes mechanism. But the system could quickly converges to the stable condition under dynamic subsidies and taxes. The stable point is the situation of corporate low-carbon innovation, government regulation, and public supervision. Third, the public intervention and supervision can effectively prevent the phenomenon of government misconduct and enterprises over-emission production. And the influence of public reward and punishment is more effective for the government than for enterprises. • A tripartite evolutionary game of low-carbon innovation system among enterprises, government and the public. • Dynamic subsidy and tax mechanisms drive the model to converge to the ideal state. • Increased government subsidies will promote low-carbon innovation, but it isn't the more the better. • The same degree of public supervision is more effective for governments than for enterprises. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness. In this paper, we built models with different parameters to predict carbon trading prices, and proposed models with high accuracy and robustness. The accuracy of the models was assessed using traditional survey indicators. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0.0133 shortening of the Z-score range. Furthermore, the CNN-LSTM model achieved a level of accuracy comparable to other popular models such as CEEMDAN, Boosting, and GRU. It also demonstrated a training speed improvement of at least 40% compared to the aforementioned methods. These results suggest that the CNN-LSTM enhances model resilience. Moreover, the practicality of using Z-score to evaluate model robustness is confirmed. • Deep learning by combined CNN-LSTM networks for carbon price forecasting. • Hyperparameter tuning of deep learning models for improving forecast accuracy. • CNN-LSTM models outperform single CNN models and single LSTM models. • Performance metric of deep learning for abrupt changes in carbon price variation. [ABSTRACT FROM AUTHOR]