High Carbon Emissions Research Articles

Climate Risk Disclosure and Financial Performance of High Carbon Emission Companies in China

Climate Risk Disclosure and Financial Performance of High Carbon Emission Companies in China

Mixture composition design of magnesium oxychloride cement-stabilized crushed stone materials applied as a pavement base

Conventional binding materials, such as silicate cement and lime, present high energy consumption, pollution, and carbon emissions. Therefore, we utilize crushed stone as a stabilization material. Magnesium oxychloride cement (MOC) is modified and used as an inorganic admixture owing to its eco-friendly nature and low carbon content. We analysed the control indicators of an integrated design of MOC-stabilized crushed stone by conducting unconfined compressive strength and water-resistance tests. The optimum mixing composition of the MOC-stabilized crushed stone was determined through the response surface methodology. We determined the best approach and dosage for improving the water resistance of MOC-stabilized crushed stone by comparing the effects of four modification methods: fly ash, citric acid + silica fume, phosphoric acid + waterborne polyurethane, and dihydrogen phosphate potassium salt. We also perform a comparison with 5% ordinary silicate cement-stabilized crushed stone. The results indicate that the MOC-stabilized crushed stone exhibits a rapid increase in strength in the early stage, but this rate reduces after 28 days. The mixing design employs the 4-day unconfined compressive strength and 1-day water resistance coefficient as the technical indicators. The best mixing composition includes a 4.27% MOC dosage and a molar ratio of MgO/MgCl2 of 5.85. We use 1% citric acid + 10% silica fume in equal amounts to replace the MOC dopant method for composite modification of the MOC stabilized crushed stone. Consequently, the 1-day water resistance coefficient before water immersion is significantly increased from 0.78 to 0.91 and its 4-day unconfined compressive strength is only reduced by 0.10 MPa. This significantly improves the water resistance of the MOC-stabilized crushed stone and ensures that its strength remains unaffected, which is the optimal modification method. However, this method must ensure that a small amount of citric acid and silica fume are uniformly distributed in the MOC-stabilized crushed stone, which increases the construction difficulty of the road base.

Distributed prosumer trading in the electricity and carbon markets considering user utility

With the promotion of distributed energy trading and the transformation of power system operation mode, prosumers in the carbon and electricity markets face new opportunities and challenges. This paper proposes a peer-to-peer transaction platform to promote renewable energy consumption and carbon emission reduction, including multiple types of loads, coal-fired units, and microgrid prosumers. The carbon-emitting members of power generation and demand sides in the electricity market are introduced into the carbon market, and their carbon cost model is established. To guide consumption behavior of demand side, the user utility model based on electricity consumption is built to reduce carbon emissions. With the goal of optimizing the generation cost, user utility, and network fees, the distributed optimal scheduling model is established to recover the network fees. Finally, the alternating direction method of multipliers is used to solve this problem and protect transaction privacy. The results indicate that introducing carbon trading at the power generation and demand sides can promote the shift of members' production behavior from high-carbon emission industries to low-carbon ones. Additionally, an appropriate network fee mechanism and unit fee can guide market members to adjust their trading behavior adaptively and help the power grid recover costs.

Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China

Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China

Global optimization strategy of prosumer data center system operation based on multi-agent deep reinforcement learning

The escalating issues of high energy consumption and carbon emissions in data centers (DCs) necessitate the optimization of system operations. However, early optimization strategies were overly simplistic and lacked automated updating and iterative capabilities. With the evolution of artificial intelligence (AI), researchers have applied deep reinforcement learning (DRL) algorithms to system operations. However, the optimization focus has been limited to the internal systems, lacking global optimization. In this paper, a global optimization control strategy based on the Dueling double-deep Q network (D3QN) and value decomposition network (VDN) algorithms is proposed to make the DCs system operate more closely with the upstream, midstream, and downstream. By adjusting battery charging/discharging capacity, computational workload, and waste heat utilization heating temperature global synergistic optimization is achieved. Compared with without optimization, renewable energy waste, operation cost, total electricity consumption, and grid electricity consumption are reduced by 18.37%, 9.78%, 4.01%, and 29.74%, respectively. Additionally, a detailed comparison between non-algorithmic optimization and algorithmic optimization is provided, offering valuable insights for substantial energy savings and emissions reduction in DCs. The results demonstrate the importance of fully exploring the interactive potential between upstream energy supply, midstream computational workload, and downstream waste heat recovery to achieve synergistic global optimization of “computing power", “thermal energy" and “electrical energy" for the sustainable and green development of DCs or other prosumer buildings.

Low-carbon economic scheduling for AIES considering flexible load coordination and multiple uncertainties

The disordered energy use in traditional agricultural production has resulted in high energy purchase costs and carbon emissions. The coupling of integrated energy systems with agriculture offers the potential for flexible coordination of agricultural loads while agricultural loads are highly influenced by changes in the natural environment. In this context, this paper proposes a low-carbon and economic scheduling optimization method for agricultural integrated energy system (AIES), which comprehensively takes temperature, light intensity, and the coordination of agricultural loads into consideration. Firstly, solar and biomass resources are integrated through energy conversion equipment to support energy supply from both the electrical distribution network (EDN) and the gas network. At the same time, the modelling process of agricultural load is refined, which is divided into crop irrigation, greenhouse temperature control and residential consumption. The models of agricultural multi-energy complementary and energy storage equipment are established to achieve flexible conversion between different energy sources. In addition, the distributionally robust optimization (DRO) method based on multiple discrete scenarios is adopted to deal with the uncertainties arising from environmental condition fluctuations during the optimization process. Finally, the proposed model is tested on 4 various regions and 2 different seasons. Numerical results show that the proposed model can effectively enhance the economic and environmental performance of the AIES.

Digital carbon neutrality: evidence of carbon emission reduction based on digital inclusive finance.

As a powerful engine for economic reform and curbing carbon emissions, digital inclusive finance provides solid support for achieving the goal of digital carbon neutrality. This study reveals the positive effect of digital inclusive finance on carbon emission reduction and the deeper reasons behind it by digging deeper into the panel data of 213 cities in China. The study adopts advanced empirical analysis methods to rigorously test the association between digital inclusive finance and carbon emissions. The results show that there is a strong positive correlation between the booming development of digital inclusive finance and the significant decline in carbon emissions. This finding remains solid after several rounds of robustness tests, which fully proves the reliability of the research results. Further mechanism analysis reveals the multiple paths of digital financial inclusion on carbon emission reduction. First, it promotes the optimization and upgrading of industrial structure by optimizing the allocation of financial resources, thus reducing the proportion of high-carbon emission industries. Second, digital inclusive finance attracts more foreign capital inflows and introduces advanced low-carbon technologies and management experience, further promoting the development of low-carbon economy. In addition, the study also found that the differences between different cities in terms of geographic location and city size have a significant impact on the carbon emission reduction effect of digital inclusive finance. In particular, the carbon emission reduction effect of digital inclusive finance is particularly significant in western regions, central cities, and first-tier cities. In response to these findings, this paper proposes a series of targeted policy recommendations. First, the financial service system should be further optimized to increase the coverage and penetration of digital inclusive finance, especially in less developed regions and small- and medium-sized cities. Second, regional policy synergies should be strengthened to form a strong synergy to promote the development of a low-carbon economy. In addition, it should guide capital flows to low-carbon industries and encourage enterprises to increase green technology research and development and application, while actively promoting low-carbon consumption concepts and guiding consumers to form green consumption habits. Through the implementation of these measures, it is expected that the potential of digital inclusive finance in the development of a low-carbon economy will be further stimulated, making a greater contribution to the realization of the goals of carbon peaking and carbon neutrality.

A novel strategy to enhance heavy oil Recovery: Condensation heat transfer calculation and 2-D visualized physical simulation

High carbon emissions and low efficiency in thermal utilization are pivotal challenges in heavy oil development. Addressing the urgent need to enhance heavy oil production, this paper introduces an innovative approach in which aerogel and flue gas serve as supplementary additives in steam thermal recovery. To investigate the mechanisms behind the improved heat transfer and oil recovery facilitated by the combination of aerogel and flue gas, condensation heat transfer experiments and 2-D visual oil displacement experiments were carried out. The condensation heat transfer coefficient, oil displacement performance, and heat utilization efficiency were analyzed and compared. The results showed the porous structure of the aerogel enhances its thermal insulation properties by adsorbing flue gas, thereby reducing the steam-to-cold object condensation heat transfer coefficient by 60.6%. The cementation between aerogel nanoparticles (NPs) retained in the formation and rock particles formed an insulating layer that impedes the upward propagation of steam heat. Combined with efficacy of flue gas foam in improving fluid flow, this promoted the horizontal expansion of the steam chamber, resulting in an 8.2% and 3.4% increase in the steam chamber’s extent compared to using steam and a combination of steam with aerogels, respectively. Moreover, the profile control and thermal insulation impacted following the NPs, lowering the water cut in the produced fluid and enhancing the oil recovery by 7.9%. Upon the application of flue gas, this enhancement in recovery extended to 9.3%.

Decoupling economic growth from carbon emissions: How have Chinese provinces performed in green growth over time?

Extensive research has shown that it is essential to take environmental responsibilities into careful consideration in economic development. Green growth has thereby become one of the most important topics in environmental economics across the globe no matter people are from academia, public, or private sectors. Even though there are many researchers who have investigated the relationship between economic growth and environmental pollution, the vast majority of research has only focused on the absolute amount of economic growth and carbon emissions. In this study, utilizing national statistics about gross domestic product and carbon emissions by region over decades, I not only provide a concrete and comprehensive picture of the green growth of Chinese provinces over time, but also present multiple indicators to capture the growth of carbon emissions and gross domestic product, as well as the growth rate of green growth. The results show that, in spite of a high GDP growth rate, the green growth rate was negative because the carbon emissions growth rate was much higher than the rate of GDP growth. This work has shed some light regarding how Chinese provinces can balance between high economic growth and low carbon emissions in order to have a more sustainable and environment-friendly development path.

Integrated optimization modelling framework for low-carbon and green regional transitions through resource-based industrial symbiosis

The development and utilization of bulk resources provide the basic material needs for industrial systems. However, most current resource utilization patterns are unsustainable, with low efficiencies and high carbon emissions. Here, we report a quantitative tool for resource-based industries to facilitate sustainable and low-carbon transitions within the regional economy. To evaluate the effectiveness of this tool, the saline Qinghai Lake region was chosen as a case study. After optimizing the industrial structure, the benefits of economic output, resource efficiency, energy consumption, solid waste reduction, and carbon emission reduction can be obtained. The scenario analyses exhibit disparities in different transition paths, where the carbon mitigation, economic output, and resource efficiency that benefit from optimal development paths are significantly better than those of the traditional path, indicating the urgency of adopting cleaner technology and industrial symbiosis for regional industries.

Effective hydrogen supply chain management framework considering nonlinear multi-stage process uncertainties

The large amount of carbon emitted due to industrial development is posing serious climate change. Under these circumstances, hydrogen energy is being used as a major energy source and is attracting attention as an alternative energy to fossil fuels, characterized by high carbon emissions. While the use of hydrogen has the obvious benefit of a low carbon footprint, the hydrogen production process possesses a high carbon footprint difference depending on the source of utilized electricity. This study considers several situations arising in an electrolysis-based hydrogen supply chain network (HSCN). A multi-stage stochastic programming (SP) is modeled to consider the uncertainty of demand and transportation capacity generated in HSCN. Moreover, a typical stochastic model has a chain rule between the decision variables resulting from nonlinearity and uncertainty, and these features make it difficult to derive an effective solution. Thus, in this study, a new methodology, Weighted Scenario Sample Average Approximation (WSSAA), is proposed to derive an effective solution for the multistage SP model considering various scenarios under the hydrogen supply chain. While a general stochastic programming approach fails to generate feasible solutions with volatile HSCN uncertainties, the proposed WSSAA framework provides feasible decisions considering multiple scenarios. The effectiveness of the proposed model applied with WSSAA is demonstrated through comparative experiments over the models used in existing studies.

Research on the allocation scheme of carbon emission allowances for China's provincial power grids

The power industry is the industry with the highest carbon emissions in China, and the rational allocation of carbon quotas for the power industry is the key to the rational allocation of carbon quotas in China, while the transfer of carbon emissions generated by inter-provincial power trading makes it difficult to calculate the carbon emission quotas for power. The article calculates the comprehensive power carbon quota for each province based on the principles of equity(PEQ) and the principles of efficiency(PEF). The results of the study prove the following points. (1) There are significant differences in the carbon emission factors calculated for electricity generation and consumption in each province. (2) The western provinces with abundant electricity resources deliver electricity to the eastern and southern provinces to ensure their economic development, so the western provinces with higher inflows of electricity carbon emissions bear the carbon emission pressure of the eastern and southern provinces. (3) The provincial electricity carbon quota calculated by the principle of comprehensiveness(PC) takes into account social equity and economic efficiency, and the calculation results are more reasonable. This study achieves balanced regional development by reasonably allocating provincial electricity carbon quotas.

Evaluating and simulating the impacts of land use patterns on carbon emissions in coal resource-based regions: A case study of shanxi province, China

It is essential for sustainable development worldwide to reduce carbon emissions (CE), however, how land use patterns in coal resource-based regions (CRBRs) affect CE remains less explored. Based on socioeconomic data and GlobeLand30, this study evaluated and predicted the influence of land use patterns on CE in Shanxi Province from 2000 to 2030. The key findings are as follows: (1) From 2000 to 2020, the area of cropland in the study area decreased by 4.85 × 105 ha and the area of construction land increased by 4.38 × 105 ha, with the increase in construction land coming mainly from cropland. (2) There was an increasing trend in the overall net CE, from 3.17 × 107 t in 2000 to 3.89 × 107 t in 2010 and 6.38 × 107 t in 2020. The land use CE had a spatial distribution characteristic of being high in the west and south and low in the east and north. The total CE was the largest in 2030 in business-as-usual scenario (BAUs), and the CE class of the cities in the study area was mainly high CE, while the ecological and economic balance scenario (EEBs) was higher CE. In 2030, the BAUs will have the largest total CE, and the CE levels of the cities will mainly high CE. (3) The decoupling state between the land use mix degree (LUM) and CE in the study area was mainly an expansionary negative decoupling over the past 20 years and changed from long negative decoupling to weak decoupling in the economic development priority scenario (EDPs) and strong decoupling in EEBs, which might be better suited to the future development of Shanxi Province. This study makes it easier to comprehend how different land use patterns affect the number of CE in CRBRs. Additionally, it provides other CRBRs with a theoretical framework and instances needed to research CE.

Low-Carbon Ecological Tea: The Key to Transforming the Tea Industry towards Sustainability

The realization of the value of ecological products has led to an economic means for reducing carbon emissions in China. Tea is one of the most important cash crops and one of the most popular beverages in the world. Due to the complex the tea industrial chain, it is considered to be an industry with high carbon emissions. Ecological tea products with low-carbon attributes can be considered a linkage of ecology, economy, and society. Based on this, this paper presents research on low-carbon ecological tea (LCT). Herein, we construct the formational logic of low-carbon ecological products, explore the connotations of LCT, and form a conceptual pathway for realizing LCT to contribute to climate change mitigation and adaptation. This paper starts from the upstream, midstream, and downstream of the industrial chain; it establishes three value realization pathways that keep, as a priority, the promotion of ecological industrialization, focus on restoration to improve the ecology of the industrial chain, and innovate technology to expand the industrial chain. The pathways are a set of low-emission production solutions that use techniques to enhance carbon sequestration in soil, reduce the use of fertilizers and pesticides, and help shift to clean energy from low-emission sources in the stages of plantation, processing, and distribution. In the process of realizing LCT, the government plays an important role, and its support and guidance are needed. Based on stakeholder theory, this paper builds an implementation mechanism that focuses on the micro perspective (users, organizations), integrates the mesoscopic perspective (industry), and relies on the macro perspective (government).

Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system.

Optimization of preparation techniques for high-temperature resistant waterborne phenolic-epoxy resin emulsion under low carbon background

In light of escalating global climate change concerns and the pressing need to address industries with high carbon emissions and pollution, enhancing the preparation of phenol-formaldehyde epoxy resins has emerged as a critical research focus. This study seeks to fabricate waterborne phenol-formaldehyde epoxy resins with superior performance by investigating pivotal factors influencing their properties and refining preparation methods. Utilizing tetrabutylammonium bromide as a phase transfer catalyst, the phenol-formaldehyde epoxy resins are synthesized via a two-step alkalization process. Subsequent etherification reactions involve modifying the phenol-formaldehyde epoxy resins using cationic modifier diethanolamine (DEA) and anionic modifier sodium p-amino benzenesulfonate, resulting in waterborne phenol-formaldehyde epoxy resins. Subsequently, in situ synthesis is employed to produce nanoscale silica (SiO2) modified waterborne phenol-formaldehyde epoxy resins. The findings reveal that when the ratio of n1 to n2 falls within the range of 1/3.25 to 1/3, the emulsion displays a moderate particle size and maintains stable storage. Furthermore, an increase in DEA dosage leads to a particle size of less than 324 nm when the ratio of n1 to n2 exceeds 1/3, indicating stability. Moreover, optimal stability and prolonged storage lifespan are achieved when the nano SiO2 content is approximately 1.5%. This study contributes by synthesizing high-quality waterborne phenol-formaldehyde epoxy resin emulsions through optimized methods. The research findings offer a theoretical foundation for this domain and support the practical application of low-carbon and environmentally friendly concepts in the coatings industry.

Parameter Tuning for Sustainable 3D Printing (3DP) of Clay Structures

Due to high carbon emissions, traditional construction methods are often costly, resource-intensive, and environmentally harmful. This study investigates the potential of additive manufacturing (AM) or 3D printing (3DP) using earthen clay as a sustainable alternative for building structures. Despite its promising benefits, challenges such as water/clay ratio, nozzle clogging, and temperature variations hinder widespread adoption. Employing the design of experiment (DOE) approach, this research systematically optimizes 3DP parameters, including nozzle diameter, layer height, infill percentage, and printing speed, using commercial earthen clay. Chemical compositions are analyzed using X-ray photoelectron spectroscopy (XPS) and energy-dispersive spectroscopy (EDS). Quantitative analysis compares measured dimensions of 3DP structures to assess dimensional accuracy, while buildability assessments explore structural integrity. Results indicate that lower layer heights generally enhance dimensional accuracy, with larger nozzle diameters enabling faster printing speeds. ANOVA analysis reveals significant relationships between printing parameters and dimensional outcomes, emphasizing the importance of 3DP process parameters selection. Optimal printing parameters lead to high-quality prints, although with limitations in scalability. This research contributes valuable insights for stakeholders seeking sustainable 3DP practices in construction, paving the way for more environmentally friendly and cost-effective building solutions.

Study on the Properties and Hydration Mechanism of Calcium Carbide Residue-Based Low-Carbon Cementitious Materials

Alkali-activated cementitious materials, as an environmentally friendly cementitious material, can effectively reduce carbon emissions and improve the utilisation of solid wastes. However, traditional strong alkali activators have limitations such as high carbon emissions and poor safety. In order to overcome the defects of traditional strong alkaline activators and realise the high value-added use of calcium carbide residue (CCR), this paper adopts CCR as an alkaline activator to activate granulated blast furnace slag (GBFS)-steel slag (SS) cementitious systems for the preparation of alkaline-activated cementitious materials. The effects of CCR content and SS content on the compressive strength and working performance of CCR-GBFS-SS cementitious systems are analysed, along with the hydration process of CCR-GBFS-SS cementitious systems and the mechanism of action through the hydration products, their chemical structure and their microscopic morphology. The research results show that CCR-GBFS-SS cementitious systems have a 28-day compressive strength of 41.5 MPa and they can be controlled by the setting time; however, the flow performance is poor. The SS content can be increased to improve the flow performance; however, this will reduce the compressive strength. In CCR-GBFS-SS cementitious systems, CCR is the main driving force of hydration reactions, GBFS mainly provides active silica and aluminium and the amorphous C-(A)-S-H gel and ettringite formed by the synergistic action of multiple solid wastes are the main sources of compressive strength. With the extension of the curing time, the amount of hydration products in the cementitious systems gradually increases and the matrix of the cementitious systems gradually becomes denser. This study will provide a reference for the consumption of low-value solid waste such as CCR and the preparation of low-carbon cementitious materials from multi-component solid wastes.

Preliminary Review of Adaptive Reuse Practice for Heritage Buildings

The economy is measured based on the development of a city and the new development will involve costs for material, energy, and time which also generates high carbon emissions. It is important to have guidelines to retain and sustain the heritage development and reduce the reality of climate change due to the urgency towards sustainability. This paper aims to provide knowledge contribution and awareness of the adaptive reuse for heritage buildings in line with heritage preservation and sustainable design. Besides, important factors to be considered for adaptive reuse in heritage buildings that can be focused as one of the important maintenance strategies for heritage development The method used in this research is qualitative method which is by carrying out semi structured interview, case study, and observation. The observation has been done towards the case study to identify the principles and processes of adaptive reuse applied. The findings existing factors of adaptive reuse practices in the case study, significant factors of reuse for heritage buildings, and recommendations to develop awareness to protect heritage buildings. Many factors influenced the implementation of adaptive reuse, including the building's state, ineffective management, and the building's purpose should be considered to improve the preservation process for better value of the building.

Risk Assessment Analysis of Hybrid Renewable Energy Systems in Remote Areas: Case Study of Domadgee, Australia

This study comprehensively analyses the energy requirements and options for Doomadgee, a remote indigenous community in Australia. Currently, the electricity provider relies on a 2.44 MW diesel power generator, resulting in high operational costs and carbon emissions. This study explores transition options to renewable energy sources to address these challenges while ensuring reliable and cost-effective power supply. This study suggests utilising a 2.4 MW wind energy source, a 1.2 MW solar energy source, and a 4 MW battery storage system that can last 8 hours. This configuration aims to reduce diesel consumption, optimise the internal rate of return (IRR), and establish a sustainable energy mix with low capital expenditure. The wind and solar capacities recommended for Doomadgee also consider seasonal fluctuations, such as wet and dry seasons in energy demand, ensuring efficient power production all year round. The study commences with the development of a risk matrix to determine the hurdle rate. The next step involves conducting ten distinct scenarios to evaluate the investment's financial feasibility, considering various variables such as wind, solar, equipment, and battery storage capacities. Sensitivity analysis also determines the most significant factors impacting financial valuation. This analysis provides valuable insights that can aid in the decision-making process.