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This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China's impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path "innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy" has significantly positive direct and indirect effects in both the treatment group and the control group. However, "innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector's impossible triangle coupling coordination degree" and "innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector's impossible triangle coupling coordination degree" have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O (n 2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution. [ABSTRACT FROM AUTHOR]

Investigating the degree of coupled energy environment (EE) and socioeconomic (SE) coordination is becoming a commonly understood necessity in the process of achieving SDGs. The provinces along the Yangtze River Economic Belt (YREB) in China have significant spatial variations in their energy endowment, industrial structure, and ecological environment, and assessing the spatiotemporal evolution and coordination of EE and SE can provide valuable insights into the development trajectory of the area. This work revealed the interaction mechanism between EE and SE and developed a novel coupling coordination analysis framework, which integrated the dynamic deviation maximum method and the improved coupling coordination degree model. The dynamic deviation maximum method is used to evaluate the performance of the EE subsystem and SE subsystem, while the improved coupling coordination degree (CCD) model is used to assess the spatiotemporal effect between EE and SE in the YREB. The results show that the overall development of CCD between EE and SE demonstrated a growing trend over time and forms a spatial pattern of downstream>midstream>upstream. Additionally, the investment intensity of industrial structure and environmental management has a catalytic effect on CCD, whereas industrial pollution emissions, energy consumption, and fixed asset investment intensity have a suppressive effect. These findings can provide valuable insights for decision-making in urban planning, large-scale engineering construction, energy structure transformation, and green low-carbon development. [ABSTRACT FROM AUTHOR]

This review underscores the pivotal role of Cognitive Systems (CS) in enhancing energy efficiency within the industrial sector, exploring the application of sophisticated algorithms, data analytics, and machine learning techniques to the real-time optimization of energy consumption. This methodology has the potential to reduce operational expenses and further diminish environmental repercussions; however, it also leverages data-driven insights and predictive maintenance to foresee equipment malfunctions and modulate energy utilization accordingly. The viability of integrating renewable energy sources is emphasized, supporting a transition towards sustainability. Furthermore, this research includes a bibliometric literature analysis from the past decade on the deployment of CS and Artificial Intelligence in enhancing industrial energy efficiency. [ABSTRACT FROM AUTHOR]

In recent times, there have been notable advancements in solar energy and other renewable sources, underscoring their vital contribution to environmental conservation. Solar cells play a crucial role in converting sunlight into electricity, providing a sustainable energy alternative. Despite their significance, effectively optimizing photovoltaic system parameters remains a challenge. To tackle this issue, this study introduces a new optimization approach based on the coati optimization algorithm (COA), which integrates opposition-based learning and chaos theory. Unlike existing methods, the COA aims to maximize power output by integrating solar system parameters efficiently. This strategy represents a significant improvement over traditional algorithms, as evidenced by experimental findings demonstrating improved parameter setting accuracy and a substantial increase in the Friedman rating. As global energy demand continues to rise due to industrial expansion and population growth, the importance of sustainable energy sources becomes increasingly evident. Solar energy, characterized by its renewable nature, presents a promising solution to combat environmental pollution and lessen dependence on fossil fuels. This research emphasizes the critical role of COA-based optimization in advancing solar energy utilization and underscores the necessity for ongoing development in this field. [ABSTRACT FROM AUTHOR]

As a chemical product with rapidly expanding demand in the field of modern energy and environmental applications, hydrogen peroxide (H2O2) has garnered widespread attention. However, the existing industrial production of H2O2 is plagued by high energy consumption, harmful waste emission, and severe safety issues, making it difficult to satisfy the environmental/economic production concept. Artificial photosynthesis offers a viable strategy for green and sustainable H2O2 production since it uses sunlight as an energy source to initiate the reaction of oxygen and water to produce H2O2. Among various photocatalysts, covalent organic frameworks (COFs), featuring highly ordered skeletons and well‐defined active sites, have emerged as promising photocatalysts for H2O2 production. This review presents the nascent and burgeoning area of photocatalytic H2O2 production based on COFs. First, a brief overview of photocatalytic technology is provided, followed by a detailed introduction to the principles and evaluation of the photocatalytic H2O2 generation. Subsequently, the latest research progress on the judicious design of COFs for H2O2 photosynthesis is expounded, with a particular emphasis on manipulating the electronic structures and redox active sites. Finally, an outlook on the challenges and future opportunities is proposed, in the hope of stimulating further explorations of novel molecular‐designed COFs for sustainable photosynthesis. [ABSTRACT FROM AUTHOR]

Based on the Sustainable Development Goals outlined in the 2030 agenda of the United Nations, affordable and clean energy is one of the most relevant goals to achieve the decarbonization targets and break down the global climate change effects. The use of renewable energy sources, namely, solar energy, is gaining attention and market share due to reductions in investment costs. Nevertheless, it is important to overcome the energy storage problems, mostly in industrial applications. The integration of photovoltaic power plants with hydrogen production and its storage for further conversion to usable electricity are an interesting option from both the technical and economic points of view. The main objective of this study is to analyse the potential for green hydrogen production and storage through PV production, based on technical data and operational considerations. We also present a conceptual model and the configuration of a PV power plant integrated with hydrogen production for industry supply. The proposed power plant configuration identifies different pathways to improve energy use: supply an industrial facility, supply the hydrogen production and storage unit, sell the energy surplus to the electrical grid and provide energy to a backup battery. One of the greatest challenges for the proposed model is the component sizing and water electrolysis process for hydrogen production due to the operational requirements and the technology costs. [ABSTRACT FROM AUTHOR]

An improved design was proposed by incorporating helical fins onto the vortex finder, and the impact on the comprehensive performance of the oil‐gas cyclone separator was analyzed based on flow field characteristics. The results revealed that the integration of helical fins led to a slight enhancement in the overall separation efficiency and a significant reduction in pressure drop, thus contributing to the decrease of energy consumption in industrial processes. The orthogonal experimental design (OED) method was utilized for the discussion and optimization of five structural parameters related to the helical fins. This study provides new insights for the design of internal components of oil‐gas separators. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is regarded as a key path to tackling global warming and achieving the Sustainable Development Goals (SDGs). In 2020, the energy consumption of the world's ten major energy-consuming countries accounted for 66.8% of the global total. This paper applied data envelopment analysis (DEA) to calculate these ten major energy-consuming countries' total-factor energy efficiency (TFEE) at national and sectoral levels from 2001-2020, and explored the influencing factors of total-factor energy efficiency with the Tobit regression model. The results showed significant difference in the ten countries' energy efficiency. The United States and Germany topped the list for total-factor energy efficiency, while China and India were at the bottom. Meanwhile, the energy efficiency of the industrial subsector has increased significantly over the past two decades, while that of the other subsectors has been relatively flat. The industrial structure upgrading, per capita GDP, energy consumption structure, and foreign direct investment had significant impacts on energy efficiency with national heterogeneity. Energy consumption structure and GDP per capita were determinative factors of energy efficiency. [ABSTRACT FROM AUTHOR]

The global energy crisis, which began in 2021 due to the extraordinary economic recovery after the pandemic and intensified after Russia's invasion of Ukraine in February 2022, has changed the conditions of energy management, paying more attention to energy efficiency. Natural gas prices have reached record levels and, consequently, so have electricity prices in some markets. Oil prices have reached their highest level since 2008. Higher energy prices have contributed to sharply increased inflation. Households are again becoming interested in buying coal as a source of heat. High energy and gas prices have pushed many families into poverty and forced some factories to cut production or even close. They have also slowed economic growth to the point where some countries are heading for a serious recession. Paradoxically, the negative effects of the energy crisis may accelerate the introduction of cleaner, sustainable, renewable energy such as wind and solar energy. The energy crisis is comparable to the oil crisis of the 1970s, when it contributed to significant advances in energy efficiency. The current crisis has highlighted the importance of investments in renewable energy resources and initiated the process of integrating regional markets, developing energy efficiency and promoting renewable energies. The aim of this article is to comprehensively explore the complex relationship between energy awareness, consumption patterns, and energy efficiency, with a focus on both individual consumers and industries, during the global energy crisis. This paper is based on a literature review, overarching policy documents, energy reports, and other secondary documents. The primary research method was the systematic literature review method, based on which the impact of the global energy crisis on energy efficiency was evaluated. This study emphasizes the diverse influences on energy awareness, ranging from economic factors to consumer preferences and environmental consciousness. The findings of the paper underscore the significant responsibility of industries in contributing to energy-saving efforts and the active role of consumers in the energy market. The responsibility of industries in contributing to energy efficiency is highlighted, with a call for a comprehensive approach that integrates energy-saving criteria into product development and corporate social responsibility. [ABSTRACT FROM AUTHOR]

A major goal of the "14th Five-Year Plan" phase is to promote the green transformation of industrial enterprises to address the 'dual carbon' challenge. Utilizing the China Industrial Enterprises Database and the Polluting Enterprises Database, this paper calculates the carbon emissions of Chinese industrial enterprises from 2001 to 2010 at the micro level. It presents an analysis of the heterogeneity of carbon emission efficiency (TPI) in industrial enterprises, as well as the factors influencing corporate TPI. This study finds that enterprises within a subdivided industry exhibit heterogeneous levels of TPI, with carbon emissions largely affected by the structure of energy consumption. The researchers suggest accelerating the transition of industrial enterprises to green technology and argue that carbon emission policies should shift from controlling direct total targets to strengthening market-oriented policy tools. Carbon reduction targets should be more stringent for enterprises with lower TPI, considering the heterogeneity among enterprises. To meet the challenges of emission reduction, industrial enterprises are encouraged to actively reform their energy consumption structure. Government policies should aim to reduce clean energy costs and encourage the use of clean energy by industrial enterprises. [ABSTRACT FROM AUTHOR]

In this paper, the combustion behavior of the syngas/air mixture in a narrow gap disk reactor were explored. Besides, the influence of the hydrogen volume fraction and the mixture equivalence ratio in syngas on the property of propagating flames were investigated in the narrow gap disk reactor under the gap thicknesses of 8 mm, 6 mm, 4 mm, 3 mm, and 2 mm, respectively. The focus was placed on the behavior of the flame propagation under the gap thickness of 2 mm. The research results demonstrated that the increase of the hydrogen volume fraction from 5 % to 30 % exerted a significant impact on the flame propagation velocity (FPV) and the pressure in the tube. Moreover, the equivalence ratio of this mixture could markedly affect both the propagation characteristics and morphology of flames. Strong flame instabilities accompanied by pressure oscillations were observed in the reactor at a 2 mm gap thickness during the later stages of flame propagation. Notably, there were significant differences in unstable combustion behavior between different syngas components. Moreover, the combined action of heat losses and compression waves leads to significant flame instability. The insights gained from this study have practical implications in various production applications. For example, optimizing hydrogen volume fraction and mixture equivalence ratios can enhance combustion efficiency and reduce pressure fluctuations in industrial reactors, leading to improved energy utilization and reduced environmental emissions. • The combustion characteristics of syngas in a narrow gap disk reactor were studied. • Hydrogen content affects flame propagation but has little effect on cell structures. • The syngas/air mixture equivalence ratio affects the flame cell structure. • Strong instability manifests when the flame propagates within a 2 mm gap thickness. [ABSTRACT FROM AUTHOR]

A dynamic STIRPAT model used in the current study is based on panel data from the eight most populous countries from 1975 to 2020, revealing the nonlinear effects of urbanization routes (percentage of total urbanization, percentage of small cities and percentage of large cities) on carbon dioxide (CO2) emissions. Using "Dynamic Display Unrelated Regression (DSUR)" and "Fully Modified Ordinary Least Squares (FMOLS)" regressions, the outcomes reflect that percentage of total urbanization and percentage of small cities have an incremental influence on carbon dioxide emissions. However, square percentage of small cities and square percentage of total urbanization have significant adverse effects on carbon dioxide (CO2) emissions. The positive relationship between the percentage of small cities, percentage of total urbanization and CO2 emissions and the negative relationship between the square percentage of small cities, square percentage of total urbanization and CO2 emissions legitimize the inverted U-shaped EKC hypothesis. The impact of the percentage of large cities on carbon dioxide emissions is significantly negative, while the impact of the square percentage of large cities on carbon dioxide emissions is significantly positive, validating a U-shaped EKC hypothesis. The incremental effect of percentage of small cities and percentage of total urbanization on long-term environmental degradation can provide support for ecological modernization theory. Energy intensity, Gross Domestic Product (GDP), industrial growth and transport infrastructure stimulate long-term CO2 emissions. Country-level findings from the AMG estimator support a U-shaped link between the percentage of small cities and CO2 emissions for each country in the entire panel except the United States. In addition, the Dumitrescu and Hulin causality tests yield a two-way causality between emission of carbon dioxide and squared percentage of total urbanization, between the percentage of the large cities and emission of carbon dioxide, and between energy intensity and emission of carbon dioxide. This study proposes renewable energy options and green city-friendly technologies to improve the environmental quality of urban areas. [ABSTRACT FROM AUTHOR]

The ever-increasing consumption of fossil fuels to meet up with the global economic and industrial energy needs has led to climatic change due to uncontrollable emission of a major greenhouse gas (CO2). As a way of mitigating the amount of CO2 in the atmosphere, search for effective and efficient solid adsorbent has been at the front burner of current scientific research. A class of solid adsorbent known as metal organic frameworks (MOFs) have demonstrated immense potentials for CO2 adsorption due to its porous, high thermal and chemical stability, high versatility and ease of production. Upon functionalization, the adsorption efficiency of this class of materials was found to improve tremendously. In this review, the CO2 capture and sequestration potentials of three MOFs (UiO-66, HKUST-1, and MOF-5) and their composites were investigated in the search for economical, stable, and highly selective novel adsorbents for CO2 adsorption. [ABSTRACT FROM AUTHOR]

Industrial robots, like all machines, require energy to operate, which is why energy efficiency in industrial robotics has been a subject of consideration in recent years in many scientific and industrial centers. Interest in the topic is especially noticeable in Industry 4.0. Research on energy efficiency stems from the emergence of new possibilities in terms of making strategic decisions related to manufacturing processes. As energy-efficient production is an essential part of sustainable development, the energy efficiency of industrial robots must be considered. The need to reduce costs while maintaining quality and increasing production efficiency has necessitated the implementation of modern solutions aimed at reducing electricity consumption. The rational use of electrical energy, especially in the industrial sector, significantly reduces production costs and, consequently, contributes to a company's profits and competitiveness. This article aims to provide an overview of energy efficiency issues based on recently published articles. This article discusses the appropriate selection of robots, their programming, energy-efficient trajectory planning, and the monitoring of the operation of the robotic system to minimize energy consumption. Typical industrial applications of robots are also mentioned and discussed. [ABSTRACT FROM AUTHOR]

Thermal processes represent a significant fraction of industrial energy consumptions, and they rely mainly on fossil fuels. Thanks to technological innovation, highly efficient devices such as high-temperature heat pumps are becoming a promising solution for the electrification of industrial heat. These technologies allow for recovering waste heat sources and upgrading them at temperatures up to 200 °C. Moreover, the coupling of these devices with thermal storage units can unlock the flexibility potential deriving from the industrial sector electrification by means of Demand-Side Management strategies. The aim of this paper is to quantify the impact on the energy system due to the integration of industrial high-temperature heat pumps and thermal storage units by means of a detailed demand–supply model. To do that, the industrial heat demand is investigated through a set of thermal process archetypes. High-temperature heat pumps and thermal storage units for industrial use are included in the open-source unit commitment and optimal dispatch model Dispa-SET used for the representation of the energy system. The case study analyzed is Belgium, and the analysis is performed for different renewable penetration scenarios in 2040 and 2050. The results demonstrate the importance of a proper sizing of the heat pump and thermal storage capacity. Furthermore, it is obtained that the electrification of the thermal demand of industrial processes improves the environmental impact (84% reduction in CO2 emissions), but the positive effect of the energy flexibility provided by the heat pumps is appreciated only in the presence of a very high penetration of renewable energy sources. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

The energy demand increased dramatically owing to the evolution of industrial and domestic requirements and the associated decrease in oil and gas resources. This study aims to evaluate El-Maghara coal (with about 52 MT reserve) as a potential hydrocarbon source. The collect samples were subjected to petrographic, chemical analyses and Rock–Eval pyrolysis to investigate the detailed characteristics of this coal. Chemically, this coal is high volatile bituminous coal with high H and S content. The high H/C ratio indicates the high extraction yield of coal. The main maceral group in the studied samples is vitrinite (62.8%) followed by liptinite (31.3%) and inertinite (5.8%). The content of liptinite indicates the capability of this coal for petroleum production. Based on Rock–Eval Pyrolysis results and TOC content, the coal has excellent petroleum potential. The hydrogen index (HI) and H/C atomic ratio indicate the II kerogen type (oil prone) of this coal. This coal has Tmax and vitrinite reflectance values around 415.8 °C and 0.37%, respectively, indicating the immature stage of kerogen. The high reactive maceral content (94.2%), oil-yield (65.5%) and conversion from coal to oil (95.4%), indicated that this coal has a hydrocarbon generation potential for oil. [ABSTRACT FROM AUTHOR]

The global aluminium industry faces a serious challenge in reducing greenhouse gas emissions as the demand for aluminium continues to increase. The aluminium industry has a responsibility to streamline its energy consumption, especially in the production process. There have been many studies discussing energy consumption performance in the industry. However, most of them only discuss energy saving partially, without involving energy consumption with various items produced. This paper proposes an energy savings measurement in the manufacturing industry. An energy baseline consumed per unit volume has been developed using the equivalent volume method with an energy management control system (EMCS). The study takes a case example from the automotive--aluminium component industry. The steps taken in the study are: examining the production process, converting the production volume to equivalent, calculating the energy consumption ratio, developing an energy baseline, simulating the savings performance, and then proposing an EMCS with key performance indicators (KPI) for sustainability. The results show that the development of a baseline using the ratio of energy and an equivalent volume of production gives a better data correlation with an R² value close to one. From the baseline, the bestdemonstrated performance (BDP) can be used as a reference to set energy goals. Furthermore, the data and the deviant deleted have the same baseline value. They differ in energy reduction goal percentages. The practical application of this study is not only in the manufacturing industry but in other industries as well, such as building management. This study contributes to energy savings achieved with EMCS. [ABSTRACT FROM AUTHOR]

Dust accumulation on solar panels decreases its efficacy due to the reflection of radiation by dust particles, which further decreases the amount of radiation reaching the solar cells. Antireflective superhydrophobic coatings based on nano-silica and nano-titania were prepared and applied on glass slides and small solar panels for laboratory scale study. All the coated substrates showed hydrophobic to superhydrophobic nature as confirmed by the contact angle of water drops on the coated glass. Scanning electron microscopy and atomic force microscopy results confirmed the presence of nanostructures on coated surfaces, which was responsible for the superhydrophobic behavior. The coating was also found to be stable in acidic, neutral, and basic solutions and a slight change in contact angle was observed after dipping the coated glass in different pH solutions. The antireflective nature of coated solar panels was also observed, and it was found that the coated side of the solar panel showed less reflection of light compared to the uncoated side. The current–voltage curve (I–V curve) was plotted for uncoated and coated solar panels and, further, fill factor and efficiency were calculated. Dust repelling and cleaning efficiency were also evaluated, and it was found that different types of dust were easily removed from coated panels without leaving behind any scratch marks. From the results, it can be concluded that the antireflective and superhydrophobic coatings are simple and sustainable solutions for cleaner and more efficient solar panels. [ABSTRACT FROM AUTHOR]

Among earthen construction techniques, cob might be an interesting solution to mitigate greenhouse gases emissions and energy consumption of the building industry. One main issue encountered is that the cob material shows large variability of hygrothermal properties, which could consequently have an impact on the reliability of the estimation of the energy consumption of cob buildings. At the wall scale, the hygrothermal properties significantly influence the kinetics of moisture and heat transfers through the building shell, both being coupled. In order to measure the relative contribution of the variation of the hygrothermal properties, a sensitivity analysis of a coupled heat and moisture transfer model has been carried out on a cob wall. More specifically, a local sensitivity analysis has been performed (one model input wobbles around a reference value) and compared with a global sensitivity analysis, which may provide the potential interaction between model inputs. For the latter approach, the Morris method was used and allows to find the influence level of material properties and the relationships with model outputs. Two study cases have been performed: a static loading case, to find temperature and water vapour pressure profiles across the cob wall until the steady state and a dynamic loading case under a 2.5 years external dynamic loading (St-Nazaire meteorological data, France). As main results, the global approach showed in general a higher variability of properties, the sorption isotherms and the water vapour permeability were the most influential input parameters on humidity profiles while on temperature ones, the variability of both properties led up to 0.25 °C variation range. The influence of thermal properties was very sensitive to the daily-loading variation while that of the hygric properties was very sensitive to the seasonal-loading variation. [ABSTRACT FROM AUTHOR]

The use of microalgae Chlorella kessleri VKPM A1-11 ARM (RF, NPO Algobiotechnology) for environmental and energy purposes is considered. The results of our study of the use of a biomass of C. kessleri microalgae as a biosorbent to purify model wastewater from Cu2+ ions under static conditions are presented. Biosorption is a promising technology for the treatment of industrial effluents containing various heavy metal compounds, but the issues of economic benefits of using biosorbents, their environmental safety, and the cost of disposal of waste sorbents are subject to much discussion. This paper proposes to utilized the biosorbent formed after wastewater treatment from copper as an additional fuel. The copper concentration in the filtrate was determined by colorimetric analysis with sodium diethyldithiocarbamate. The cleaning efficiency and sorption capacity of the dry mass of C. kessleri were obtained by calculation. The maximum sorption capacity for Cu2+ ions was 4.2 mg/g. The purification efficiency reached 87% at an initial concentration of Cu2+ ions of 97 mg/L. Tests to estimate the specific heat of combustion of the C. kessleri biomass and waste biosorbents based thereon were carried out by the calorimetric method using a bomb calorimeter. The specific heats of combustion were 22 125 and 21 674 kJ/kg, respectively. A comparison of these values with traditional energy carriers is given. A technological scheme was developed for a waste-free cycle of using C. kessleri to treat wastewater from industrial enterprises with the production of several valuable resources as end products, such as purified water, energy resources, fertilizers, and recycled metals. The results of our study can be applied in technologies for post-treatment of wastewater from various industrial enterprises using biological nonwaste resources. [ABSTRACT FROM AUTHOR]

Fruit and vegetable processing comes 6th in terms of energy consumption in the agri-food industry. At the same time, 88.4% of the industry's final energy consumption structure is thermal energy, which depends heavily on electricity consumption. In addition, fruit and vegetable processing has a significant impact on the environment due to consumption of significant amounts of water. Reducing these three indicators simultaneously would increase the efficiency of the process while improving environmental protection. This paper proposes neural models of thermal energy, electricity and water consumption for selected major fruit- and vegetable-processing plants in Poland. These models were the basis for formulating a multi-criteria optimization task. Optimization of thermal energy, electricity and water consumption was carried out using genetic algorithms. The optimization results in the sense of Pareto can be the basis for the use of sustainable technology in selected fruit- and vegetable-processing plants. [ABSTRACT FROM AUTHOR]

Optimal energy management of microgrids enables efficient integration of renewable energies by considering all system flexibilities. For systems with significant seasonal imbalance between energy production and demand, it may be necessary to integrate seasonal storage in order to achieve fully decarbonized operation. This paper develops a novel model predictive control strategy for a renewable microgrid with seasonal hydrogen storage. The strategy relies on data-based prediction of the energy production and consumption of an industrial power plant and finds optimized energy flows using a digital twin optimizer. To enable seasonal operation, incentives for long-term energy shifts are provided by assigning a cost value to the storage charge and adding it to the optimization target function. A hybrid control scheme based on rule-based heuristics compensates for imperfect predictions. With only 6% oversizing compared to the optimal system layout, the strategy manages to deliver enough energy to meet all demand while achieving balanced hydrogen production and consumption throughout the year. • PV with seasonal H 2 storage enables fully sustainable operation of industrial microgrids. • Model predictive control overcomes perfect foresight limitation in energy management. • H 2 production for long-term storage is incentivized by adding an extra cost term. • Hybrid predictive control enables a low cost system with nearly perfect yearly operation. [Display omitted] [ABSTRACT FROM AUTHOR]

As people put forward higher demand for the effect and quality of building interior decoration, the attention to building energy-saving materials has become higher and higher. In recent years, there have been many researches on green building energy-saving technology, green building evaluation and other aspects, but there are relatively few researches on applying the concept of full life cycle cost to the performance analysis of new materials for building energy conservation and environmental protection. Considering the economic performance of buildings will help to save resources in the construction industry and reduce energy consumption. The so-called full life cycle optimization of energy-saving design of green buildings is to take the concept of full life cycle optimization as the most reference, fully coordinate all elements in the portfolio project, and ensure that the full life cycle cost of the building can be greatly reduced. To realize the energy-saving optimization of green buildings, it is necessary to ensure that it runs through the whole life cycle of the building, fully combine the life cycle cost with the building function, and systematically do the optimization work. The performance analysis model of building decoration energy-saving and environmental protection new materials based on the whole life cycle proposed in this paper can be used to analyse the various properties of various new materials. The experimental results show that the maximum error of the test results is 9.6%, the minimum error is 1.3%, and the average error is only 6.43%. This shows that the model built in this paper is reliable, and the calculated results are in good agreement with the experimental results, which can be used to analyse the performance of new building decoration energy-saving and environmental protection materials. The ideas and methods of solving the model established in this paper have important guiding significance, and can be used as a powerful reference tool for studying other solving methods. [ABSTRACT FROM AUTHOR]

Anaerobic digestion of vegetable waste is a viable approach for sustainable waste management. However, the process requires optimization. Using a batch anaerobic reactor with cabbage waste as fermentation material, the potential and characteristics of methane production during anaerobic digestion of vegetable waste at five different temperatures were investigated. The analysis revealed that the cumulative gas production increased with rising temperature and was 528.33 mL at 30 ℃; 927.5 mL at 35 ℃; 955 mL at 40 ℃; 1017.5 mL at 45 ℃; and 1075 mL at 50 ℃; the cumulative methane production at the five temperatures was 233.18, 569.14, 597.62, 437.88, and 621.65 mL, respectively, with the lowest production at 45 ℃. Methane production rates per total solid content were 97.69, 238.43, 250.37, 183.44, and 260.43 mL/g, accordingly. Methane production rates per volatile solid content were 180.00, 439.34, 461.33, 338.02, and 479.88 mL/g, respectively. Overall, the methane generation during anaerobic digestion of cabbage waste at a temperature-blind zone of 45 ℃ was relatively low. Considering the need to reduce energy consumption in industrial processes, 35 ℃ is the most suitable temperature for anaerobic digestion of cabbage waste. The study provides a theoretical basis for efficient cabbage waste utilization and industrialization of related applications. [ABSTRACT FROM AUTHOR]

Energy sources are of paramount significance in the contemporary landscape, categorically classified into two main types: (i) primary sources, encompassing a wide spectrum ranging from nuclear energy to fossil fuels like natural gas and oil; and (ii) renewable sources, including geothermal, hydropower, solar, and wind energies. Governments have taken proactive measures since 2010, culminating in the establishment of the Bureau of Energy Efficiency under the Energy Conservation Act, aimed at curtailing energy consumption across diverse economic sectors. The interconnectedness of energy consumption, environmental ramifications, and economic progress is undeniable. A noteworthy project originating in 2010 is rooted in the pioneering market-based mechanism known as the perform, achieve, and trade (PAT) framework, which predominantly targets industrial energy utilization. Given the substantial role of energy costs within the broader spectrum of total production expenses, it becomes imperative to gauge the profit margin intensity (PMI) within energy-intensive sectors and industries encompassed by the PAT initiative. This entails an exploration of the influence exerted by these sectors on PMI. Consequently, the identification of variables influencing industrial profitability with respect to energy employment becomes pivotal. This article introduces a methodology grounded in panel data analysis, applied to a specific case study involving Indian energy-intensive corporations. The investigation takes into account the impact of both the Energy Conservation Act (ECA) and PAT as dichotomous covariates. Notably, the ambit of PAT encompasses the eight most energy-intensive industries in India, spanning the years 2012 to 2015. India stands among the world's foremost energy consumers, with its industrial sector notably emerging as the largest energy consumer in 2015. Evidently, energy serves as a driving force behind the country's manufacturing costs. The findings of this study underscore a negative correlation between energy costs and profitability. While the overall impact of PAT on industry performance appears limited, the ECA emerges as a potent factor significantly affecting earnings. Moreover, a compelling indirect relationship between energy costs and profitability is discerned, wherein rising revenues correspondingly lead to amplified energy costs. Consequently, the implications drawn from our study are intricately linked to the efficacy of energy utilization regulations within energy-intensive industrial contexts. The statistical analyses integral to this study were diligently carried out using the R software. [ABSTRACT FROM AUTHOR]

Highly selective adsorptive separation of propylene/propane (C3H6/C3H8) using metal–organic frameworks (MOFs) is a promising approach to reducing energy consumption in the chemical industry. Here, we report a 3D anionic MOF obtained by the exchange of dimethylamine cations with small K+ ions. 77 K N2 isotherms proved that the pore volume increased from 0.30 cm3 g−1 to 0.42 cm3 g−1 after ion exchange. Moreover, at near room temperatures (273 K and 298 K), both single-component gas adsorption capacity and selectivity of C3H6/C3H8 were enhanced. The C3H6/C3H8 separation potential was further confirmed by breakthrough experiments with equimolar C3H6/C3H8 mixtures. Similar separation properties were also observed in ethylene/ethane separation. [ABSTRACT FROM AUTHOR]

The application of thermal energy storage (TES) has been proved effective to improve the energy utilization efficiency of renewable energy and industrial waste heat energy. In this paper, a modified one-dimensional dimensionless model for the thermocline thermal energy storage tank is derived to simulate the system more accurately. An adaptive strategy for solving region compartmentalization is proposed for reducing computing time. Based on the proposed model, the effects of three different parameters on the performance of the thermocline tank are studied. The results show that increasing the inlet velocity can reduce the thickness of the thermocline and improve the system efficiency. Increasing the temperature difference between hot and cold water leads to a thicker thermocline, but the thermal energy stored in the tank increases. Increasing the tank height has no effect on the motion characteristic of thermocline, but the system efficiency can be increased. [ABSTRACT FROM AUTHOR]

Energy-awareness in the industrial sectors has become a global consensus in recent decades. Green scheduling is acknowledged as an effective weapon to reduce energy consumption in the industrial sectors. Therefore, this paper is devoted to the green scheduling of flexible manufacturing cells (FMC) with auto-guided vehicle transportation, where conflict-free routing of the vehicles is considered. To deal with this problem, a bi-objective optimization model is proposed to achieve the minimization of the maximum completion time and the total energy consumption in an FMC. The studied problem is an extension of flexible job shop problem which is NP-hard. Thus, an improved bi-objective salp swarm algorithm based on decomposition (IMOSSA/D) is proposed and applied to the problem. The approach is based on the decomposition of the bi-objective problem. Salp swarm intelligence along with three stochastic-distribution-based operators are incorporated into the approach, to enhance and balance its exploring and exploiting ability. Computational experiments are performed to compare the proposed approach with two state-of-the-art algorithms. This study allows the decision makers to better trade-off between energy savings and production efficiency in flexible manufacturing cellular environment. [ABSTRACT FROM AUTHOR]

The amount of medical waste generated has increased enormously since the COVID-19 outbreak. An incineration process is the main method that is usually used to treat this waste, causing an increase in both medical waste bottom ash (MWBA) and medical waste fly ash (MWFA). In this work, the physical and chemical characteristics of MWFA and MWBA were reviewed. This ash contains high levels of polychlorinated dibenzo-p-dioxin (PCDD), dibenzofurans (PCDFs), and heavy metals. Furthermore, medical waste ash appears to have high leachability in the toxicity characteristics leaching procedure (TCLP) test and the European standard test (EN 12457). Owing to its toxicity, medical ash can be treated using various methods prior to disposal based on the covered review. These techniques include chemical, supercritical fluid, cement-based, melting, microwave, and mechanochemical techniques. The shortcomings of some of these treatment methods have been identified, such as the emission of high levels of chlorine from the melting technique, limited applications of the flotation method on the industrial scale, long-term stability of leachate treated by cement-based methods that have not been confirmed yet, and high energy consumption in the supercritical technique. This review also covers possible applications of medical waste ash in cement production, agriculture, and road construction. [ABSTRACT FROM AUTHOR]

Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant. [ABSTRACT FROM AUTHOR]

The prevalence of substantial inductive/capacitive loads within the industrial sectors induces variations in reactive energy levels. The imbalance between active and reactive energy within the network leads to heightened losses, diminished network efficiency, and an associated escalation in operating costs. Therefore, the forecasting of active and reactive energy in the industrial sector confers notable advantages, including cost reduction, heightened operational efficiency, safeguarding of equipment, enhanced energy consumption management, and more effective assimilation of renewable energy sources. Consequently, a range of specialized forecasting methods for different applications have been developed to address these challenges effectively. This research proposes a stacked ensemble methodology, denoted as Stack-XGBoost, leveraging three distinct machine learning (ML) methods: extra trees regressor (ETR), adaptive boosting (AdaBoost), and random forest regressor (RFR), as foundational models. Moreover, the incorporation of an extreme gradient boosting (XGBoost) algorithm as a meta-learner serves to amalgamate the predictions generated by the base models, enhancing the precision of the active/reactive energy consumption forecasting using real time data for steel industry. To assess the efficacy of the proposed model, diverse performance metrics were employed. The results show that the proposed Stack-XGBoost model outperformed other forecasting methods. Additionally, a sensitivity analysis was conducted to assess the robustness of the proposed method against variations in input parameters. [ABSTRACT FROM AUTHOR]

As essential materials, iron and steel, which form the core of many national economies, guarantee the survival and development of the country. However, the iron and steel industry is also a typical example of an industry with high energy consumption and high pollution levels. Against the backdrop of a double carbon and green environment, the steel industry still needs to achieve high‐quality development. The sintering and pelletizing processes are one of the limiting links that need to be broken through. This article focuses on the source, change process, and emission modes of sulfur in the sintering and pelletizing processes, and also systematically introduces the migration behavior and law of sulfur, along with several typical desulfurization processes. Finally, the future green development of the iron and steel industry is considered. [ABSTRACT FROM AUTHOR]

Dielectric heating is one of the most promising alternatives to conventional thermal treatment of fluid foods. Higher thermal efficiency and better heating uniformity of radio frequency and microwave processes have been proven successful in providing a similar or better bacterial and enzymatic inactivation in liquid and semi-solid foods while improving the sensory and nutritional qualities of the fresh product when compared to conventional pasteurization. However, further investigations are necessary to advance scaling up of applications at different frequencies and to better understand heat distribution and energy consumption of industrial dielectric heating operations. [ABSTRACT FROM AUTHOR]

In developing regions that engage in agricultural and industrial activities, access to high-quality water for drinking, domestic, irrigation and industrial purposes is limited. In this study, comprehensive water quality assessment has been carried out to unravel the suitability of water supplies in Umunya suburb for human consumption, industrial and irrigation purposes. Water samples from spring, streams, and boreholes were analysed for physicochemical properties and heavy metals pollution. Pollution index of groundwater (PIG) was used to assess the drinking suitability of the waters whereas several indexical methods were integrated to unravel their suitability for irrigation and industrial purposes. The pH (4.61–6.53) revealed that majority of the samples are acidic. All the chemical ions were within their respective standard allowable limits, except for Fe, Pb and Ni. Generally, these heavy metals significantly impacted the water quality of the area. The PIG evaluation of the waters for drinking purpose revealed that 40% of the samples are unsuitable. However, most of the indices used for the irrigation water quality evaluation showed that majority of the samples are suitable for irrigation uses. The industrial water quality assessment indicated that the natural waters have more corrosive tendency than scaling tendency. Corrosivity and scaling indices, such as Langelier index, Larson-Skold index, chloride-sulphate mass ratio, aggressive index, Ryznar stability index, and Puckorius scaling index, unveiled that the natural waters are severely corrosive, having the tendency to damage domestic, irrigational, and industrial water distribution systems. This paper provides significant information necessary for the management and sustainability of water resources in Umunya suburb. [ABSTRACT FROM AUTHOR]

The present research has numerically investigated the heat transfer and thermal performance in a pipe with hollow and perforated spherical turbulators. Investigating the effect of aerodynamic turbulators with the same blockage percentage is a new topic for increasing heat transfer and reducing energy consumption in different industries. The used turbulators are spherical and have circular, oval, square and hexagonal holes. The best performance was obtained with the turbulator with an oval hole of 1.26. Then the effect of this turbulator with three different diameter ratios (DR = 0.65, 0.85, and 0.95) and three perforation indices (PI = 7%, 16%, and 24%) was studied. Air is used as a working fluid in turbulent flow, in the range of Reynolds number 6000 to 27,000. The present study has been analyzed using Ansys Fluent 19 software in three dimensions. The according to the results, the Nusselt number range is 1.58–5.02, and the friction coefficient is 3.55–13.99 times compared to the plain pipe in the studied geometries have increased, respectively. The thermal performance coefficient are in rangr of 0.77–2.13, and the highest thermal performance coefficient is obtained in DR = 0.85 and PI = 7%. [ABSTRACT FROM AUTHOR]

Poor air quality is linked to numerous adverse health effects including strokes, heart attacks, and premature death. Improving energy efficiency in the industrial sector reduces air emissions and yields health benefits. One of these strategies, replacing an existing grid boiler (GB) with a combined heat and power (CHP) system, can improve a facility's energy efficiency but can also increase local air emissions, which in turn can affect health outcomes. Previous studies have considered air-emissions and health outcomes of CHP system installation at a single location, but few studies have investigated the regional air quality and health impacts of replacing an existing GB with new CHP system. This study estimates the emission changes and associated health impacts of this shift in 14 regions in the US, representing different electricity generation profiles. It assumes that one manufacturing facility in each region switches from an existing GB to a CHP system. The monetized annual US health benefits of shifting a single GB to a CHP in each of the 14 regions range from $-5.3 to 0.55 million (2022 USD), while including CHP emission control increases the benefits by 100–170% ($9,000 to 1.15 million (2022 USD)). This study also includes a sensitivity analysis, which suggests that the facility location (region, state, and county), boiler efficiency, and emission control of the CHP are key factors that would determine whether shifting from a GB to CHP system would result in health benefits or burdens. Implications: Combined heat and power (CHP) systems offer industrial facilities the opportunity to improve their energy efficiency and reduce greenhouse gas emissions. However, CHP systems also combust more fuel on site and can also increase local air emissions. This study evaluates how converting an existing grid boiler (GB) system to a CHP system (with or without emission control) affects local (from combustion) and regional emissions (from electricity consumption) and the associated health burdens in different US regions. A facility can use this study's analysis as an example for estimating the tradeoffs between local emission changes, regional emission changes, and health effects. It also provides a comparison between the incremental cost of adding SCR (compared to uncontrolled CHPs) and the NPV of the monetized health benefits associated with adding the SCR. [ABSTRACT FROM AUTHOR]

The automotive production sector plays a significant role in the energy consumption of all the industrial sphere, which currently represents approximately 38% of the total global energy use. Especially in production sites with several manufacturing lines working in parallel, the occurrence of failures and anomalies or sudden changes in the production volume may require a re-scheduling of the entire production process. In this regard, a digital twin of each phase of the process would give several indications about the new re-scheduled manufacture in terms of energy consumption and the control strategy to adopt. Therefore, the main goal of this paper is to propose different modeling approaches to a degreasing tank process, which is a preliminary phase at automotive production sites before the application of paint to car bodies. In detail, two different approaches have been developed: the first is a physics-based thermodynamic approach, which relies on the mass and energy balances of the system analyzed, and the second is machine learning-based, with the calibration of several artificial neural networks (ANNs). All the investigated approaches were assessed and compared, and it was determined that, for this application and with the data at our disposal, the thermodynamic approach has better prediction accuracy, with an overall mean absolute error (MAE) of 1.30 °C. Moreover, the model can be used to optimize the heat source policy of the tank, for which it has demonstrated, with historical data, an energy saving potentiality of up to 30%, and to simulate future scenarios in which, due to company constraints, a re-scheduling of the production of more work shifts is required. [ABSTRACT FROM AUTHOR]

Global warming, inadequate natural sources and an energy crisis are significant concerns in this century. In this context, it has become important to use efficient energy technologies and reduce energy consumption in the cooling industry. In recent years, Diffusion Absorption Refrigeration (DAR) systems have been widely studied as good alternatives to vapor compression cooling systems. In this article, the heat exchanger part located in the boiler-bubble pump region of the DAR system was numerically modeled to determine its behavior. In addition to modeling, five different working fluids were utilized in the DAR system experimentally. The first test was performed utilizing 25% NH3-H2O by mass without nanoparticles. The other four tests were repeated by adding 1% and 2% (by weight) zinc oxide (ZnO) and graphene oxide (GO) nanoparticles into 25% NH3-H2O solution, respectively. The numerically obtained results showed no significant increase in the use of ZnO nanoparticles by 1% or 2%. It was seen that GO nanoparticles made a more meaningful difference in the heat transfer increment than the ZnO nanoparticles. Furthermore, experimental results showed that the utilization of nanoparticles in a 25% NH3-H2O solution assists in disappearing refrigerant. Generally, it was determined that adding nanoparticles to the base fluid eliminates the effects of temperature changes in the system. [ABSTRACT FROM AUTHOR]

Veneer drying usually consumes a significant amount of energy including heat and electricity. The soaring energy price as well as the substantial social-environmental concerns regarding energy use have urged veneer manufacturers to adapt and become more efficient in energy consumption. Different from the physics-based methods commonly seen in the literature, this research embraced a data-driven approach to analyze and predict unit gas and electricity consumption during industrial veneer drying. Both linear regression and random forest (RF) algorithms were deployed for prediction. Based on cross-validation evaluations, the RF model with all explanatory variables slightly outperformed two linear models regarding almost all accuracy metrics, although linear models had the advantage of providing an easy-to-interpret solution. [ABSTRACT FROM AUTHOR]

Reduction in energy consumption of industrial plants is always an essential need due to high cost, CO 2 emissions and global warming caused by energy supply from fossil fuels for boilers and electricity consumers. Iran has been struggling to compensate the lack of gas supply for domestic and industrial demands in winter within the past decade despite of having large natural gas resources. One of the most economical methods to decrease energy requirements in hydrocarbon plants is process improvement and revamping by changing existing solvents. There are already 44 identical acid gas removal units under operation in the South Pars Gas Complex (located in Iran) with the same feed composition. The gas-sweetening unit in the Phase 12, operating with the solution of 45.5 wt% methyldiethanolamine (MDEA), was modeled by Aspen Plus for acid gas removal (CO 2 and H 2 S) as a rate-based model with the application of electrolyte non–random two–liquid (E-NRTL) activity model and Peng–Robinson equation of state in addition to the actual characteristics of the absorber and regenerator, and the operational conditions of the gas and liquid streams. After validation of the model by the real data from the plant, the incumbent amine solution was replaced with five proposed amine solutions – one single and four blended amine mixtures – in the same mass concentration to select the most efficient alternative from the aspects of outlet acid gas concentration, H 2 S content in the treated flash gas, required total power, reboiler power supply, and regeneration heat duty. The simulation results revealed that H 2 S was sharply absorbed on the top of the column, while CO 2 was smoothly removed from the gas phase to the MDEA and blended solutions along the absorber and it occurred at the bottom of the column for the DEA solution. The blended amine solution of 1.5 wt% sulfolane + 44 wt% MDEA (Sulfinol-M) has shown to be the most appropriate option instead of the solution of MDEA compared to other suggested absorbents, with several advantages such as higher selectivity of H 2 S over CO 2 , low temperature bulge, highest H 2 S loading, and outlet sweet gas flow rate while required total power, reboiler power supply, and regeneration heat duty were reduced by 21.19%, 21.26%, and 23.54% respectively. The operational conditions of the unit with the application of the Sulfinol-M solution were optimized to achieve three specifications (constraints) and the minimum required total power (objective function) through a sensitivity analysis and optimization using a genetic algorithm to assure the reliability of values for three independent parameters, namely, amine solution temperature, amine solution flow rate, and gas temperature. The optimum limits and values of these parameters were 47–50 °C, 220 – 240 ton/h, and 34 °C respectively. [ABSTRACT FROM AUTHOR]

As an important indicator of the new round of technological progress, industrial intelligence is an important path to achieve energy savings and carbon emission reduction under the 'dual control' system of total energy consumption and energy intensity. This study examined the effects and mechanisms of industrial intelligence on energy consumption, carbon emissions, and industrial pollution emissions in the panel data of 275 cities from 2011 to 2021. The theoretical analysis showed that the application of industrial intelligence produced three effects: the scale expansion effect, the technological innovation effect, and the industrial structure upgrading effect. The interaction of these three effects would determine the direction and degree of the effects of industrial intelligence on energy consumption, carbon emissions, and the total and intensity of industrial pollution emissions. This article adopted the fixed-effects model for benchmark regression and used instrumental variables, the GMM dynamic panel, the exogenous shock test, and the replacement of explanatory variables to ensure the robustness of the benchmark results. The results showed that: ① Industrial intelligence could not only reduce total energy consumption and industrial pollution emissions but also reduce the intensity of energy consumption, carbon emissions, and industrial pollution emissions. ② Empirical evidence showed that the impact of industrial intelligence on total energy consumption, carbon emissions, and industrial pollution emissions was less than its impact on their intensity. Industrial intelligence was primarily responsible for the 'dual control of the amount and intensity of energy consumption,' but had less impact on the 'dual control of the amount and intensity of industrial pollution emissions,' and fell short of the goal of the 'dual control of the amount and intensity of carbon emissions.' ③ The mechanism test revealed that the current industrial intelligence' s inability to achieve the goal of the 'dual control of the amount and intensity of carbon emissions' was primarily due to the low level of green technology innovation it promoted, and that adopting intelligent technology to raise this level was a crucial precondition for the transition from the 'dual control of the amount and intensity of energy consumption' to that of emissions. The upgrade of industrial structure is an important mechanism for industrial intelligence to achieve the 'dual control' goal in terms of energy savings, carbon emission reduction, and industrial pollution emission reduction. Based on the above research findings, the article proposes policies and measures to enhance the level of intelligent industries in each region, accelerate the research and development of green technologies for intelligent enterprises, and promote the transformation and upgrading of intelligent industrial structures in an orderly manner. [ABSTRACT FROM AUTHOR]

This study presents theoretical and practical contributions to the environmental dimension of enterprises' corporate social responsibility (CSR) in sustainable development. Interest in the environment is related to CSR through environmental cost optimization and energy-efficiency management. The practical stage of the research, obtained using the computer-assisted telephone interviewing (CATI) method, allowed for presenting case studies of the best practices used by international enterprises operating in Poland. This study describes the practical tools and advice companies can use to improve efficiency and environmental responsibility. The article is an in-depth study of the growing role of enterprises in shaping sustainable and socially responsible businesses and aims to assess the extent to which these companies prioritize energy efficiency as a part of their CSR initiatives. The authors highlight the role of energy efficiency in achieving broader corporate environmental responsibility. This research aims to encourage businesses to adopt responsible environmental strategies for a greener and more sustainable future. The implementation of this goal helped develop and indicate conclusions regarding the development of environmental tools related to corporate responsibility in sustainable development, encouraging scientific debates and promoting responsible monitoring of the implementation of this concept. [ABSTRACT FROM AUTHOR]

The development of the tertiary industry is of great significance for promoting industrial structure, optimizing and upgrading it, and achieving regional energy conservation and emission reduction goals. This study adopts a quantitative method to analyze the spatio-temporal pattern of carbon emissions from China's tertiary industry from 2004 to 2019. In order to analyze emissions from aspects such as energy structure, energy intensity, energy carrying capacity, industrial structure, level of industrial development, income level, consumption capacity, energy consumption intensity, and population size, this study establishes a hybrid factor decomposition model called the "energy-industry-consumption" research framework. The study shows that carbon emissions from China's tertiary industry have been increasing year by year from 2004 to 2019, with a growth rate of 353.10%. Transportation is the largest contributor to the increase in carbon emissions from China's tertiary industry. The carbon emissions from the tertiary industry in each province show four types: high-speed growth, low-speed growth, fluctuating growth, and stable growth. During the study period, carbon emissions produce a spatial heterogeneity with the highest emissions in the south and lowest in the northwestern part of China. The spatial pattern of per capita carbon emissions is not significant. Guangdong has the highest carbon emissions, and Shanghai and Beijing have higher per capita carbon emissions. Industrial factors and consumption factors have a positive effect on carbon emissions in China's tertiary industry, while energy factors have a negative effect. The leading factor of carbon emissions in China's tertiary industry has gradually shifted from energy to industry. [ABSTRACT FROM AUTHOR]

Based on China's policy of double control of energy consumption, this study adopts the Logarithmic Mean Divisia Index model to explore the potential factors driving the changes in industrial energy consumption in the Yangtze River Delta during 2005–2019. Moreover, the convergence in energy intensity is also tested with sigma, beta, and stochastic convergence methods. The results are as follows: (1) The economic output effect was the largest contributor to the increase in energy consumption, followed by the population size effect, whereas the industrial structure effect was the dominant factor in reducing energy consumption over the same period. For the four effects, the contribution of the energy intensity effect to the reduction of the total effect was greatest, occupying the overwhelming proportion of 117%. (2) The industrial energy intensity in the Yangtze River Delta exhibits varying degrees of convergence in its distribution. (3) The foreign direct investment and industrialization level make no contribution to the convergence of energy intensity. Based on obtained results, this study then provides targeted policy recommendations to effectively manage energy consumption. [ABSTRACT FROM AUTHOR]

For curbing the global climate crisis, China has set an ambitious target of peak carbon emissions by 2030. Beijing, the capital of China, has implemented a carbon reduction policy since 2012. Using the reduced and generalized forms of the Environmental Kuznets Curve (EKC), we deduce that both the cubic EKC and the genetic algorithm-based EKC have an N-shape. The first turning point of the three-order EKC occurs around 2011, demonstrating the effectiveness of the carbon reduction policy. However, the time series model predicts that Beijing will reach the second turning point around 2026, when the gross domestic product (GDP) is about CNY 5000 billion and carbon emissions will begin to increase again. Interpretable machine learning is proposed to explore the socio-economic drivers in carbon emissions, indicating that total energy consumption and GDP contribute the most. Therefore, we should accelerate the upgrading of energy consumption and adjust the industrial structure, thus facilitating Beijing to its peak carbon emissions and achieving carbon neutrality. [ABSTRACT FROM AUTHOR]