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Carbon emissions trading from past studies has been recommended as effective in minimizing future levels of carbon emissions. The aim of this paper is to develop a theoretical framework for a construction industry carbon trading system by identifying the categorizations in the system and their influences. The theoretical framework in this study was developed using the PROMISE Framework. PROMISE is an acronym representing Personal, Relational, Organizational, Market, Institutional, Social, and Environmental. The Scopus database was used in the selection of articles. Using the System Dynamics (SD) Causal Loop Diagram (CLD) approach, the positive and negative influences among the variables in the seven categories were evaluated and illustrated. This study is significant and provides a foundation for future researchers to develop conceptual frameworks and models for carbon mitigation strategies. For policy makers, the proposed carbon trading framework assists in evaluating the key legal, economic, environmental, and political policies that can improve carbon trading projects in the built environment. When policy makers place significant emphasis on the influences identified in this study, it will contribute to them supporting regulations and policies that effectively mitigate these emissions.

There have been many implementations of carbon credit incentives (CCIs) for promoting green travel, but research on quantifying the effectiveness remains limited. To fill this gap, this study focuses on residents’ daily transportation mode choices under the incentive of carbon credits by employing an integrated choice and latent variable (ICLV) model to adequately address the role of attitudinal variables related to carbon credits, such as perceived usefulness, perceived ease of use, and behavioral intentions. Data from a questionnaire survey show that the ICLV model provides a richer and more nuanced understanding of the green mode choice than a traditional multinomial logit (MNL) model, where the AIC value of the ICLV model (3901.17) is smaller than that of the MNL model (3910.09). Carbon credits exhibit diverse impacts across various modes of eco-friendly transportation and specific demographic groups. Commuting trips reveal noteworthy positive associations between carbon credits and the use of bicycles as well as metro/bus services. Moreover, carbon credits exert a more pronounced influence on individuals with higher education levels, older age groups, and owners of new energy vehicles, whereas their impact on high-income individuals is relatively constrained. Furthermore, perceptions of carbon credits are pivotal, with perceived utility emerging as the most influential factor. The results provide a scientific basis for formulating more effective policies regarding carbon credit and incentive measures in the future.

The remaining carbon budget for limiting global warming to 1.5 degrees Celsius will probably be exhausted within this decade 1 , 2 . Carbon debt 3 generated thereafter will need to be compensated by net-negative emissions 4 . However, economic policy instruments to guarantee potentially very costly net carbon dioxide removal (CDR) have not yet been devised. Here we propose intertemporal instruments to provide the basis for widely applied carbon taxes and emission trading systems to finance a net-negative carbon economy 5 . We investigate an idealized market approach to incentivize the repayment of previously accrued carbon debt by establishing the responsibility of emitters for the net removal of carbon dioxide through ‘carbon removal obligations’ (CROs). Inherent risks, such as the risk of default by carbon debtors, are addressed by pricing atmospheric CO 2 storage through interest on carbon debt. In contrast to the prevailing literature on emission pathways, we find that interest payments for CROs induce substantially more-ambitious near-term decarbonization that is complemented by earlier and less-aggressive deployment of CDR. We conclude that CROs will need to become an integral part of the global climate policy mix if we are to ensure the viability of ambitious climate targets and an equitable distribution of mitigation efforts across generations. To enable net-negative CO 2 emissions, the repayment of previously accrued carbon debt by establishing the responsibility for the net removal of CO 2 by carbon-emitting parties through carbon removal obligations is necessary.

To fully explore the regulation resources on both sides of the source and load under uncertain environment and collaboratively achieve the energy saving and emission reduction goals, a low-carbon economic optimization dispatch model combining demand response and carbon trading mechanism is proposed in this paper. Firstly, the economic principle of demand response (DR) is analyzed, as well as the demand response compensation model is constructed for shiftable loads and curtailable loads respectively. Second, we describe the source-load synergistic low-carbon effect. The source side further reduces carbon emissions by establishing a reward-punishment laddered carbon trading model. Accordingly, the optimization model is constructed with the objective of minimizing the sum of DR compensation cost, carbon trading cost and system operation cost. The triangular fuzzy method is used to deal with the uncertainty problem of new energy and load forecasting. Finally, the economic and low-carbon nature of this proposed model is verified by simulation and example analysis.

In the energy transition, there is an urgent need for decreasing overall carbon emissions. Against this background, the purposeful and verifiable tracing of emissions in the energy system is a crucial key element for promoting the deep decarbonization towards a net zero emission economy with a market-based approach. Such an effective tracing system requires end-to-end information flows that link carbon sources and sinks while keeping end consumers’ and businesses’ sensitive data confidential. In this paper, we illustrate how non-fungible tokens with fractional ownership can help to enable such a system, and how zero-knowledge proofs can address the related privacy issues associated with the fine-granular recording of stakeholders’ emission data. Thus, we contribute to designing a carbon emission tracing system that satisfies verifiability, distinguishability, fractional ownership, and privacy requirements. We implement a proof-of-concept for our approach and discuss its advantages compared to alternative centralized or decentralized architectures that have been proposed in the past. Based on a technical, data privacy, and economic analysis, we conclude that our approach is a more suitable technical backbone for end-to-end digital carbon emission tracing than previously suggested solutions.

With the realization of the “dual carbon” goal, urban public transport with an increasing proportion of new energy vehicles will become the key subject to achieve the carbon emission reduction goal. Under the new background of deep coupling between transport networks and power grids, it is of great significance to study the carbon-trading mode of urban public transport participation in promoting the development of new energy vehicles and improving the operating efficiency and low-carbon level of the “energy-transport” system. In this paper, based on blockchain technology, a framework for urban public transportation networks to participate in carbon trading is established to solve the current problems of urban public transportation’s insufficient motivation to reduce emissions, lax operation strategy and lack of carbon-trading matching mechanisms. Finally, Hyperledger Fabric was selected as the simulation platform, and we simulated the model through the calculation example. The results show that the proposed scheme can effectively improve the operating efficiency of urban public transport and reduce its operating costs and carbon emissions. In addition, policy recommendations on carbon price, carbon quota and penalties are proposed to improve the institutional system of the carbon-trading market.

Analyzing and investigating the impact of implementing an environmental policy mix on carbon emission from private cars and social welfare holds significant reference value. Firstly, based on vehicle trajectory big data, this paper employs reverse geocoding and artificial neural network models to predict carbon emissions from private cars in various provinces and cities in China. Secondly, by simulating different scenarios of carbon tax, carbon trading, and their policy mix, the propensity score matching model is constructed to explore the effects of the policy mix on carbon emission reduction from private cars and social welfare while conducting regional heterogeneity analysis. Finally, policy proposals are proposed to promote carbon emission reduction from private cars and enhance social welfare in China. The results indicate that the environmental policy mix has a significant positive impact on carbon emission reduction from private cars and social welfare. Furthermore, in the regional heterogeneity analysis, the implementation of the policy mix in eastern regions has a significant positive effect on both carbon emission reduction from private cars and social welfare, while in central and western regions, it shows a significant positive impact on social welfare but has no significant effect on carbon emission reduction in the private car sector.

As China’s carbon inclusion policies are gradually implemented, significant progress has been made in energy conservation and emission reduction. The economical use of energy is the basis for the reduction in carbon emissions, which is a direct reflection of the benefits of energy efficiency initiatives. Nonetheless, the lack of technological innovation, challenges in carbon emission monitoring, low levels of public participation, and inadequate talent cultivation present significant obstacles to the development of energy conservation and carbon inclusion. This paper, grounded in the theory of planned behavior (TPB), addresses the issue of low public participation in emission reduction initiatives. By employing a questionnaire survey, designing a 5-point Likert scale, and utilizing SPSS techniques for regression analysis and chi-square testing, this study explores and analyzes the potential factors influencing public willingness to engage in carbon emission reduction initiatives (CERIs). This research provides theoretical reference for relevant government agencies and industry insiders to formulate and implement the policies of energy saving and carbon reduction and provides targeted suggestions for China’s energy market to help them realize the sustainable development of low-carbon, energy saving, and environmental protection.

The electric power industry is not only facing the pressure from the reduction of industrial and commercial electricity prices to stimulate the significant growth of demand, but also facing the increasingly serious pressure of unreasonable consumption caused by cross-subsidies; the cross-subsidy mitigation effect and energy-saving effect of the current tiered electricity price policy have basically disappeared. This article examines the main variables that affect the electricity demand and carbon emissions in order to develop a better tiered electricity pricing scheme that can effectively manage cross-subsidies in electricity prices while simultaneously saving energy and lowering carbon emissions. The China Statistical Yearbook’s electricity balance sheets for 2013–2020 are used in this article, along with pertinent data from the State Grid Corporation of China and the China Statistical Yearbook for 2006–2016. It builds an electricity demand model for classified users by using the time series analysis method and multiple statistical regression method. The variables are then subjected to a Granger causality test and a cointegration test in this article. The analysis shows that the adjustment of the electricity price policy has a significant impact on energy-saving and carbon reduction, and for residential electricity consumption, the income variable is the main factor affecting the electricity demand. We take residents’ affordability as the constraint condition for dividing the range of electricity and determining the beneficiary group, take the carbon emission responsibility target and the cross-subsidy degree alleviation target as constraints in determining the tiered price difference, propose an improvement scheme for the tiered electricity price, and carry out the sensitivity analysis of the influence between the parameters. The results show that the optimization improves the precision of the cross-subsidy treatment and significantly improves the effects of energy conservation and emission reduction.

Guided by China’s “Dual Carbon” targets, the construction of its carbon market advances steadily. As a key policy mechanism for promoting emissions reduction and sustainable development, the emissions trading system plays a vital role in the national green transition strategy. Nonetheless, significant regional disparities exist in carbon emissions, and carbon allowance prices are subject to considerable fluctuations. This study examines the spatiotemporal evolution of China’s carbon emissions, investigating their distribution patterns across different regions. Furthermore, it analyzes the spatiotemporal changes in carbon allowance prices, focusing on their fluctuation patterns and spatial distribution, particularly regional differences in carbon market prices. This study focuses on the interplay between carbon emissions and carbon allowance prices, conducting an in-depth investigation into their interaction mechanisms. Using Shanghai as a case study, we construct a Vector Autoregression (VAR) model to empirically assess the dynamic impact of carbon emissions on carbon prices and their associated feedback effects. Subsequently, we propose policy recommendations for optimizing carbon market operations. This study enhances carbon markets’ functionality as climate governance tools, providing empirical and theoretical foundations for advancing low-carbon transitions and Sustainable Development Goals (SDGs).