Household electricity consumption in Greece: A dataset based on socio-economic features

The electricity consumption of a residence depends on many factors such as the habits and economical status of the occupants, the properties of the household and many more. To shed more light on the subject a data set for households was created. The data were collected in Greece through an anonymous survey that comprises 26 questions, resulting in 188 data points from 104 households from different time periods. Each data point contains attributes that are divided into four categories. In the first category, the information is about the household data such as the type and properties of the residence. Next, occupants' socio-economic features are gathered. In this category information for the number and type of the occupants, the employment status and the total income of the residents is included. The third category of attributes is about the energy-related occupants' behavior. Finally, the location of the household was provided from the users to estimate the weather conditions for the provided time. Data augmentation was performed to discover non-trivial relationships between the data points. Thus, a secondary set of features was computed based on the raw attributes and is also included. The provided data set can be used to extract insights that could be valuable during the imminent energy crisis.

Probing environmental sustainability pathways in G7 economies: the role of energy transition, technological innovation, and demographic mobility

Global warming remains the most devastating environmental issue embattling the global economies, with significant contributions emanating from CO₂ emissions. The continued rise in the level of greenhouse gas (GHG) emissions serves as a compelling force which constitutes the core of discussion at the recent COP26 prompting nations to commit to the net-zero emission target. The current research presents the first empirical investigation on the roles of technological advancement, demographic mobility, and energy transition in G7 pathways to environmental sustainability captured by CO₂ emissions per capita (PCCO2) from 2000 to 2019. The study considers the additional impacts of structural change and resource abundance. The empirical backings are subjected to pre-estimation tests consisting of cross-sectional dependence, second-generation stationarity, and panel cointegration tests. The model estimation is based on cross-sectional augmented autoregressive distributed lag, dynamic common correlated effects mean group, and augmented mean group for the main analysis and robustness checks. The findings reveal the existence of EKC based on the direct and indirect effects of the components of economic growth. The indicators of demographic mobility differ in the direction of influence on PCCO2. For instance, while rural population growth negatively influences PCCO2 in the short-run alone, urban population growth increases PCCO2 in the short-run and long-run periods. Nonrenewable energy, information computer technology (ICT) imports, and mobile cellular subscriptions serve as positive predictors of PCCO2, while ICT exports and renewable energy moderate the surge in PCCO2. Policy implications that enhance environmental sustainability are suggested following the empirical verifications.

Application and planning of an energy-oriented stochastic disassembly line balancing problem

End-of-life (EOL) products are getting more and more attention as a result of the rapid decline in environmental resources and the dramatic rise in population at the moment. Disassembly is a crucial step in the reuse of EOL products. However, the disassembly process for EOL products is highly uncertain, and the disassembly planning method may not produce the anticipated outcomes in actual implementation. Based on the physical nature of the product disassembly process with multiple uncertain variables, certainty disassembly cannot adequately characterize the uncertain variables effectively. Uncertainty disassembly takes into account the changes in parts caused by product use, such as wear and corrosion, which can better coordinate the arrangement of disassembly tasks and better match the actual remanufacturing process. After analysis, it was found that most of studies on uncertain disassembly focus on the economic efficiency perspective and lack of energy consumption considerations. For the gaps in the current study, this paper proposes a stochastic energy consumption disassembly line balance problem (SEDLBP) and constructs a mathematical model of SEDLBP based on the disassembly of spatial interference matrix, In this model, the energy consumption generated by the disassembly operation and workstation standby is not a constant value but is generated stochastically in a uniformly distributed interval. In addition, an improved social engineering optimization algorithm that incorporates stochastic simulation (SSEO) is proposed in this paper to effectively address the issue. The incorporation of swap operators and swap sequences in SSEO makes it possible to solve discrete optimization problems efficiently. A comparison of a case study with some well-tested intelligent algorithms demonstrates the efficacy of the solutions produced by the proposed SSEO.

A new truck-drone routing problem for parcel delivery by considering energy consumption and altitude

The high population density in metropolitan areas, high-rise buildings, and changes in people's lifestyles have completely changed the way postal packages are delivered. People no longer go to the ground floor to receive a postal package. In the meantime, the delivery of postal packages through the balconies and windows of the units on the upper floors of the buildings will gradually become inevitable. Hence, a new Vehicle Routing Problem with Drone mathematical model has been developed with the objective of minimizing total delivery time and with the ability to deliver postal packages in the path of drones at different heights. In addition, the drone's energy consumption is computed by taking into account wind speed, the weight of the postal parcel, the weight of the drone's body, and other factors in the drone's path. A two-phase algorithm based on the nearest neighborhood and local search is presented to solve the developed mathematical model in different instances. Several small-sized test problems are designed and solved, and the performance of the heuristic approach is evaluated compared to the outputs of the CPLEX solver. Finally, the proposed model is implemented on a real-world scale to demonstrate the efficacy and applicability of the proposed model as well as the heuristic approach. The results show that the model successfully finds the optimal planning of the delivery routes, especially when we deal with delivery points at different heights.

Decomposing the decoupling relationship between energy consumption and economic growth in China's agricultural sector

Sustainable food supply is affected by high energy consumption and negative environmental effects. Regarding the national strategy of "carbon peaking and carbon neutrality targets", the decoupling between energy consumption and economic growth in China's agriculture has received significant attention. Therefore, this study first presents a descriptive analysis of the energy consumption in China's agricultural sector from 2000 to 2019, before analyzing the decoupling state between energy consumption and agricultural economic growth at the national and provincial levels using the Tapio decoupling index. Finally, the logarithmic mean divisia index method is used to decompose the decoupling driving factors. The study draws the following conclusions: (1) At the national level, the decoupling of agricultural energy consumption from economic growth fluctuates among expansive negative decoupling, expansive coupling, and weak decoupling, before stabilizing in the last state. (2) The decoupling process also differs by geographic region. Strong negative decoupling is found in North and East China, and strong decoupling lasts longer in Southwest and Northwest China. (3) The factors driving decoupling are similar at both the levels. The economic activity effect promotes the decoupling of energy consumption. The industrial structure and energy intensity effects are the two main suppressive factors, whereas the population and energy structure effects have relatively weaker impacts. Therefore, based on the empirical results, this study provides evidence for regional governments to formulate policies on the relationship between the agricultural economy and energy management from the perspective of effect driven policies.

Impact of green technological innovations on environmental quality for Turkey: evidence from the novel dynamic ARDL simulation model

The contribution of this research is to provide empirical evidence that investing in green technology innovation (GTI) can reduce the ecological footprint in Turkey, which can lead to sustainable economic growth and environmental quality. The research also highlights the importance of controlling energy consumption, GDP, trade openness, and urbanization, as these variables have a positive or negative effect on ecological footprint. The findings of this research can be useful for the Turkish government, policymakers, and environmentalists to promote the implementation of GTI and eco-friendly resources, which can reduce the impact of climate change and contribute to economic prosperity. Overall, this research provides important information for decision-makers to adopt policies that prioritize green innovation and environmental protection in Turkey.

Removal of COD from real oily wastewater by electrocoagulation using a new configuration of electrodes

In order to bring the chemical oxygen demand (COD) concentration down to safe levels for widespread use, this study plans to use a state-of-the-art electrocoagulation reactor (ECR) to treat real oily wastewater discharged from the Al-Muthanna petroleum refinery. A one-side finned (1SF) cathode tube was positioned between two tubular anodes in the continuous ECR, where the active area of the cathode was much more than its submerged volume. Each of these electrodes was made of aluminum and joined in a monopolar parallel to a DC power supply. On COD elimination efficiency, the impacts of operational parameters such as electrolysis time (4-60 min), current density (0.630-5.000 mA/cm²), and flow rate (50-150 ml/min) were explored. In conclusion, Increasing current density and electrolysis duration increases COD removal efficiency, whereas increasing flow rate reduces it. COD removal efficiencies were 82% at optimal electrolysis times of 60 (min), 5 (mA/cm²) current density, and 50 (ml/min) flow rate, with energy consumption of 4.787 (kWh/kg _COD) and electrode consumption of 0.544 (g). The investigation results demonstrated that the new reactor could treat oily wastewater within the specified operational limits. It might be used before other, more conventional treatments.

Characterization of reciprocation membrane bioreactor on treatment performance, energy consumption and membrane fouling

Two reciprocating membranes (rMBR) with two frequencies of 0.46 Hz (rMBR-0.46) and 0.3 Hz (rMBR-0.3) were operated to compare the treatment performance and gross energy consumption with a conventional MBR. The average organic removal rates of MBR, rMBR-0.46 and rMBR-0.3 were maintained 295 ± 51; 823 ± 296; and 397 ± 129 mgCOD/gVSS.d, respectively. Nitrogen removal was enhanced in rMBR phases compared to conventional MBR phase due to anoxic membrane chamber. Further, fouling rate was found to be highest of 16.5 mbar/day (at conventional MBR phase), which was and much decreased to1.0 mbar/day (at rMBR-0.46 phase) and then 0.2 mbar/day (rMBR-0.3 phase). The reciprocation membrane also showed energy potential by saving 10.6% electricity for each treated cubic meter of wastewater compared to the conventional MBR.

Integration of hydrothermal and pyrolysis for oily sludge treatment: A novel collaborative process

In this study, hydrothermal treatment and in situ pyrolysis were combined to develop a novel collaborative process (HCP treatment method). In a self-designed reactor, the HCP method was used to study the influences of hydrothermal temperature and pyrolysis temperature on the product distribution of OS. The products from the HCP treatment of OS were compared with that from the traditional pyrolysis. In addition, the energy balance in the different processes of treatment was analyzed. The results showed that compared to the traditional pyrolysis method, the gas products obtained after HCP treatment achieve a higher H₂ production. As the hydrothermal temperature raising from 160 to 200 °C, the H₂ production showed an increase from 4.14 to 9.83 ml/g. In addition, GC-MS analysis showed that the content of olefins from the HCP treatment oil was increased from 1.92% to 6.01% compared to traditional pyrolysis. Energy consumption analysis showed that only 55.39% energy consumption of traditional pyrolysis is required for treating 1 kg OS by employing the HCP treatment at 500 °C. All results indicated that the HCP treatment is a clean production process of OS with low energy consumption.

Methodological framework for Life Cycle Assessment of sustainable aviation (SA) systems

A comprehensive framework is proposed for Life Cycle Assessment (LCA) in the field of commercial aviation (passengers and cargo), capable to ensure transparency and comparability when evaluating the overall environmental performances of four emerging aviation systems, i.e., biofuels, electrofuels, electric, and hydrogen. The projected global revenue passenger kilometer (RPK) and is suggested as the functional unit for two timeframes representing near-term (2035) and long-term (2045), and for two segments, namely domestic and international. To solve the difficulty of comparing liquid fuels and electric aviation, the framework proposes a methodology to translate projected RPK into energy requirements for each of the studied sustainable aviation systems. Generic system boundaries are defined with their key activities for all four systems, with the biofuel system being sub-divided into two categories to distinguish whether it stems from residual or land-dependent biomass. The activities are grouped in seven categories: (i) conventional (fossil-based) kerosene activity, (ii) conversion processes from feedstock supply (to fuel or energy production for aircraft operation), (iii) counterfactual uses of constrained resources and displacement effects associated to co-products management, (iv) aircraft manufacture, (v) aircraft operation, (vi) additional infrastructure needed, and (vii) end-of-life management (aircraft and batteries). Considering applying regulations, the framework also includes a methodology to handle: (i) hybridization (the use of more than one source of energy/propulsion system to power an aircraft), (ii) the mass penalty affecting the number of carried passengers in some of the systems, and (iii) impacts stemming from non-CO₂ tailpipe emissions - aspects that are currently neglected in most LCA studies. The proposed framework builds upon the most recent knowledge in the field; however, some choices are dependent on upcoming scientific advances concerning e.g., tailpipe emissions at high altitude and their environmental impacts, new aircraft configuration, etc., and are subject to significant uncertainties. Overall, this framework provides a guideline for LCA practitioners addressing new energy sources for future aviation.

Energy consumption, CO2 emissions, foreign direct investment, and economic growth in Malaysia: an NARDL technique

This study seeks to ascertain whether there is an unbalanced link  between CO₂ emissions, foreign direct investment, and economic growth in Malaysia over a 40-year timeframe between 1980 and 2019. We investigated the asymmetric relationship , using  non-linear autoregressive distributed lag (NARDL) technique. The findings showed a noteworthy asymmetry between FDI, CO₂ emissions, and GDP in Malaysia. The long-term and short-term effects of negative FDI on GDP are both equivalent to 0.028 and 0.021, respectively. This suggests that, compared to short-term fluctuations, long-term negative FDI adjustments have a considerably more negative impact on economic growth. The coefficient of positive (CO₂⁺) and negative (CO₂^-) changes in economic growth is equal to 0.086 and - 0.152, respectively. It indicates that positive changes in CO₂ emissions have stronger effects in the long run than negative shocks. Considering an asymmetric association between these two variables in the short and long term, Malaysian policymakers must comprehend the dynamic relationship between FDI, CO2 emissions, and GDP to plan appropriate economic and environmental policies that will support sustainable economic development and ensure a safer environment.

Development of a novel induction-heated reactor to enhance the performance of waste tires pyrolysis

Waste tire pyrolysis (WTP) is well-known as a promising technique for waste tires (WTs) management. However, the requirements of high cost and intense energy, as well as steel wire stripping remain the major drawbacks for the widespread applications of WTP technologies. In this research, a novel induction-heated pyrolysis (IHP) reactor has been developed to enhance the performance of WTP and the results were compared with a conventional fixed bed pyrolysis (FBP) reactor. The performance of the IHP reactor has been evaluated using three different feedstocks of sidewall (no steel wire), tread section (20% steel wire), and real mode (14% steel wire) at two different sizes of A and B. The results revealed that the presence of steel wire in the IHP reactor not only reduced the effect of WTs particles size, but also improved the average heat and oil production rate up to 36 % and 60 %, respectively through better heat distribution. The minimum REC (13.03 KJ/g) was achieved for the tread section after 12 min of the IHP process. The comparison of the results of the IHP reactor and the FBP reactor proved that using IHP reactor could significantly improve the performance WTP in terms of optimum heating time, heat rate, REC and power consuming cost up to 68 %, 44 %, 56 % and 61 % respectively. The findings of this study suggest IHP reactor which eliminates the need of stripping steel wires as novel and promising approach for sustainable WTs management.

Modeling the long-term fate of polycyclic aromatic hydrocarbons (PAHs) and public health risk in Bohai Bay Sea Area, China

The target of this study was to reconstruct the historical concentration, distribution, variation, and exposure risk evaluation for EPA PAHs to the whole sea of Bohai Bay and the coastal population, by employing a specific dynamic multimedia model during 1950-2050. The unsteady-state model, driven by temporal energy activities from 1950 and sustainable scenarios based on socioeconomic development, indicated the annual emission increased by 4.6 times (from 84.8 tons to 391 tons) until 2020 and resulted in concentrations up to 5.2 times in the atmospheric compartment, and 4.9 times in seawater. Two peak concentrations in 1997 and 2014, consistent with total PAHs input revealed significant regional anthropogenic input in northern Bohai Bay (Tianjin) and southern Bohai Bay (Hebei). The peak-to-peak values of the timing concentration revealed a notably alternative increase in the south (+109.4 %-128.6 %), instead of the rapid decline in the north (-21.5 %-44.5 %). The dominant processes at air-seawater interfaces were air-seawater molecular transfer (from 38.4 % to 51.8 %), and wet deposition (from 60.5 % to 47.5 %). Under 5 shared socioeconomic pathways, the optimal scenario (SSP1) achieved a 24.7 % emission decline, an atmospheric decrease of 15.1 %-31.1 %, and 24.8 %-41.2 % mitigation in seawater during 2020-2050, and each pathway exhibited a general lessening concave in the northern developed municipality, compared with convex in the southern developing regions. The inhalation risk assessment evaluated 10 generations living on Bohai Bay coasts, with an acceptable result, while the current sustainable conceive was with meager fruition in reducing risk.

Optimizing the fuel economy of hydrostatic power-split system in continuously variable tractor transmission

The aim of this study is to enhance the fuel economy of a continuously variable tractor transmission by analyzing its energy and fuel consumption. First, we present the principle of a self-developed tractor transmission based on power splitting and examine its parasitic power characteristics. Next, we construct a mathematical model of the hydraulic system, mechanical system, and entire transmission, calibrating the model to ensure the accuracy of subsequent results. We then perform a systematic analysis of the energy and fuel consumption of the tractor transmission. Finally, we optimize the transmission through design and power matching, investigating the impact of changes in parameters and control strategies on the fuel economy of the transmission. The results indicate that fuel consumption can be reduced by 2%-14% through parameter optimization and by an additional 0%-20% through appropriate power matching.

Does excessive energy utilization and expansion of urbanization increase carbon dioxide emission in Belt and Road economies?

The phenomenal increase in global temperature and variation in climate change are the replications of nature, alarming governments to limit the emissions of greenhouse gases (GHGs) and adopt green innovation and environmental-friendly clean and green technologies. In this paper, we empirically investigate whether there are any changes in excessive consumption of energy from conventional sources, expansion of urbanization, carbon dioxide (CO₂) emissions, and economic growth in six different regions, namely, East Asia (EA), South Asia (SA), Southeast Asia (SEA), Central Asia (CA), Eastern Europe (EE), and the Middle East and North Africa (MENA), under the Belt and Road Initiatives of panel data over the period of 1985 to 2017. The empirical methods include a panel co-integration check, heterogeneity test, panel Granger causality test, pooled mean group (PMG), and augmented mean group (AMG). To verify the outcomes, robustness tests were carried out using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches. Our results confirm that CO₂ emissions are primarily influenced by excessive utilization of conventional energy, economic growth, and expansion of urbanization. The findings confirm the co-integrating relationships among the variables in all six regions. Moreover, the panel causality analysis identified a bidirectional causal relationship between energy consumption, economic growth, urbanization, and CO₂ emissions. While these results can play an instrumental role in formulating CO₂ emission policies among our selected countries, our research can also assist policymakers and governments in other developing countries implement important policy initiatives. In this regard, the findings suggest that the current environment-related polices of Belt and Road Initiatives (BRI) do not efficiently tackle CO₂ emissions. In order to achieve the CO₂ emission degradation objective, the Belt and Road countries should restructure their environment-related policies by limiting the consumption of conventional energy and expansion of urbanization. The adaptation and establishment of such a panoramic policy program can assist emerging economies to acquire consolidated and environmentally sustainable economic growth.

Integrating sustainability into production scheduling in hybrid flow-shop environments

Global energy consumption is projected to grow by nearly 50% as of 2018, reaching a peak of 910.7 quadrillion BTU in 2050. The industrial sector accounts for the largest share of the energy consumed, making energy awareness on the shop floors imperative for promoting industrial sustainable development. Considering a growing awareness of the importance of sustainability, production planning and control require the incorporation of time-of-use electricity pricing models into scheduling problems for well-informed energy-saving decisions. Besides, modern manufacturing emphasizes the role of human factors in production processes. This study proposes a new approach for optimizing the hybrid flow-shop scheduling problems (HFSP) considering time-of-use electricity pricing, workers' flexibility, and sequence-dependent setup time (SDST). Novelties of this study are twofold: to extend a new mathematical formulation and to develop an improved multi-objective optimization algorithm. Extensive numerical experiments are conducted to evaluate the performance of the developed solution method, the adjusted multi-objective genetic algorithm (AMOGA), comparing it with the state-of-the-art, i.e., strength Pareto evolutionary algorithm (SPEA2), and Pareto envelop-based selection algorithm (PESA2). It is shown that AMOGA performs better than the benchmarks considering the mean ideal distance, inverted generational distance, diversification, and quality metrics, providing more versatile and better solutions for production and energy efficiency.

The effect of institutions and urbanization on environmental quality: evidence from the Belt and Road Initiative countries using dynamic panel models

Increased globalization in urban areas raise energy consumption that leads to high carbon dioxide discharge and degrade environmental quality. Other economic activities also produce emission; however, a well-established institutional framework can overcome the issues of environmental degradation and minimize the effect of harmful factors on the environment. In this regard, this study investigates the effect of urbanization, energy consumption, and industrialization on carbon dioxide emission by taking into consideration the role of institutional quality in the Belt and Road Initiative (BRI) countries for the period of 2002 to 2019. Employing dynamic panel techniques, the results are in line with theories which show that increased urbanization, energy consumption, industrialization, and economic growth raise carbon dioxide emission and lead to environmental degradation. The study also found that international trade and political stability reduce emission; however, institutional quality as a whole positively affects carbon dioxide emission. The study also found a U-shape relationship between urbanization and carbon dioxide emission. The interaction term between institutional quality and urbanization significantly mitigates carbon dioxide emission and raise environmental sustainability. The findings of this study have considerable policy suggestions for the sample countries.

The carbon footprint response to projected base stations of China's 5G mobile network

While the rapid expansion of China's 5G mobile network helps to speed up the nation's economic and social development, it tends to release more CO₂ due to the 5G's significant energy demand, hampering sustainable development of the 5G network. Previous assessments of CO₂ emissions from China's 5G development were based on a projected 5G network ranging from six to fifteen million base stations with the absent of a convincing business model in 5G's application. Under the scenario of business-estimated six million base stations in 2030, the share of electricity consumed by China's 5G networks in 2030 could reach 8.4 % of the national total power generation, causing 0.44 GtCO₂/yr CO₂ emissions. We collected 5G base station numbers in 2020 and 2021 in 31 provinces and province-level municipalities (PLM), the period with the rapid growth of the 5G base stations in China. We linked these provincial base stations with provincial Gross Domestic Product (GDP), population (POP), and big data development level (BDDL) and established a statistical model to predict 5G base stations by 2030. The model predicted 2-5 million 5G base stations by 2030, considerably lower than the business-projected base station number. Under the model predicted 5G base stations, China's 5G network could yield 0.15-0.29 GtCO₂/yr emissions subject to the nation's BDDL from 40 to 80 % by 2030. Both 5G base stations and CO₂ emissions are significantly lower than the previous estimates. We decomposed the CO₂ footprint of China's 5G networks and assessed the contribution of the number of 5G base stations and mobile data traffic to 5G-induced CO₂ emissions. We find that increasing the application of clean energy and promoting energy efficiency can reduce CO₂ emissions in the 5G network. To more accurately estimate 5G's climate effect, we propose that it urgently needs to improve vivid 5G business models.

Impacts of financial development on the energy consumption in China from the perspective of poverty alleviation efficiency

Poverty alleviation and energy saving are two major issues of sustainable development targets. Meanwhile, financial development (FD) is a powerful engine of economic growth, which is regard as a valid approach to contain the demand for energy consumption (EC). However, few studies link the three factors and explore the specific impact mechanism of poverty alleviation efficiency (PE) on the tie between FD and EC. Thus, we employ the mediation and threshold models to evaluate the influence of FD on the EC in China during 2010-2019 from the perspective of PE. We affirm that FD indirectly promotes EC through the channel of PE. The mediating effect of PE is responsible for 15.75% of the total effect of FD on the EC. Moreover, FD generates a significant threshold impact on the EC considering the change of PE. When the PE exceeds 0.524, the role of FD in promoting EC is strengthened. Ultimately, the outcome suggests policymakers need to prominent the trade-off between energy saving and poverty reduction during the fast evolution of financial system.

Estimating environmental efficiency of the selected Asian countries: does convergence exist?

Environmental degradation has attained much attention from researchers and policymakers at national and global levels. The ever-increasing energy use in production methods is considered one of the fundamental reasons for environmental degradation. The concept of environmental efficiency in the wake of sustainable growth evolved in the last three decades. The present study has been designed to estimate environmental efficiency using the Malmquist-Luenberger productivity index (MLI) using annual data from 43 Asian countries from 1990 to 2019. The MLI is an established econometric approach to estimate cases where input variables are used to get output variables in desirable and undesirable forms. Labor, capital, and energy consumption are input variables, while carbon dioxide (CO₂) emissions (undesirable variable) and gross domestic product (undesirable variable) are taken as output variables. The results suggested that, on average, environmental efficiency has decreased by 0.3% over the period in selected Asian countries. Cambodia, Turkey, and Nepal have the highest total factor productivity (TFP) output growth rate on average among 43 Asian countries. These countries are excellent examples of sustainable development that balances environmental protection and efficiency. On the other hand, Kuwait, Mongolia, and Yemen showed the least TFP growth. The study also employed unconditional and convergence tests where the countries' conditional convergence is based on foreign direct investment, population density, inflation, industrialization, and globalization. Some policy implications for Asian countries are also discussed at the end of the study.

The moderating role of technological innovation between tourism and carbon emission: short and long-run panel analysis

In contemporary times of globalization, tourism and travel are among the fastest-growing economic sectors and are highly related to climate change; however, technological innovations as a powerful tool positively contribute to the environment. The present study examines the tourism receipt and CO2 emission relationship and the triple interaction effect of technological innovation, energy consumption, and tourism receipts on CO2 emissions in the short and long run. To achieve study objectives, we utilized panel data of 64 Belt and Road Initiative (BRI) countries over the period of 1995 to 2019. Considering the cross-sectional dependence in the panel data set, we employed a series of econometric panel data estimation techniques-including the panel unit root tests, panel co-integration tests, and the generalized method of moments (GMM). The panel unit root results confirmed the level of stationarity, and the panel co-integration results verified the long-run relationship among study variables. The sys-GMM results indicate that tourism receipts and CO2 emissions have an inverse relationship for 64 BRI countries. In addition, the negative coefficients for joint interaction imply that tourism receipts, technological innovation, and energy consumption reduce CO2 emissions. Considering the theoretical underpinnings of the study outcomes, we discussed significant policy implications to reduce CO₂ emissions and achieve sustainable tourism.

Analysis of financial support efficiency and influencing factors of listed seed companies from the perspective of energy consumption and carbon emissions

The seed industry has a prominent role in strengthening ecological stability and national food security as it provides the basic ground for agriculture sector. In the current research, the effectiveness of financial support provided to listed seed enterprises and its influencing factors from the perspectives of energy consumption and carbon emissions are examined using a three-stage DEA-Tobit model. The dataset for the underlined study variables mainly comes from the financial data published by 32 listed seed enterprises and the China Energy Statistical Yearbook from 2016 to 2021. In order to make the results more accurate, the influence of external environmental factors such as economic development level, total energy consumption, and total carbon emission on listed seed enterprises are excluded. The results revealed that the mean value of financial support efficiency of listed seed enterprises increased significantly after excluding the influence of external environmental and random factors. External environmental factors such as regional energy consumption and carbon dioxide emission played an important role in the process of the financial system supporting the development of listed seed enterprises. The development of some listed seed enterprises with high financial support efficiency came at the cost of high local carbon dioxide emission and high energy consumption. Internal factors such as operating profit, equity concentration, financial structure, and enterprise size are the key intra-firm factors that affect the efficiency of financial support for listed seed enterprises. Thus, it is suggested that enterprises must pay attention to the environmental performance to reach a win-win situation in reducing and improving the energy consumption and financial performance, respectively. Similarly, the improvement of energy use efficiency through endogenous and external innovation should be prioritized to achieve sustainable economic development.

Constant current-exponential attenuation mode: A non-traditional power supply mode for electrocatalytic oxidation

Low average current efficiency (ACE) and high energy consumption (EC) have seriously hindered the industrial development of electrocatalytic oxidation (ECO) technology. Timely adjustment of the current output according to the attenuation law of the organic pollutants concentration during the reaction process can help to solve the low electrical energy utilization problem at source. In this study, a non-traditional power supply mode with "constant current-exponential attenuation" (Mode CC-EA) was proposed and applied to intermittent ECO systems. The current is first output in a constant state and then attenuated exponentially according to the decreasing law of pollutants concentration, enabling efficient use of electrons at all stages of the reaction, resulting in increased degradation rates and ACE, and reduced EC. Acidic red G (ARG) was used as the target pollutant and the degradation effects of the traditional constant current mode (Mode CC), the direct exponential attenuation mode (Mode EA) and the Mode CC-EA were compared with different evaluation parameters. The results showed that the optimized Mode EA (n₄) and Mode CC-EA (70-n₁₁) degraded ARG with an ACE of 5.28 and 6.09%, respectively, which were 1.26 and 1.45 times higher than Mode CC (4.2%). At the same time, the EC were 0.36 and 0.27 kWh gCOD^-1, respectively, which were 12.2 and 34.2% lower than Mode CC (0.41 kWh gCOD^-1). The parameters of Mode CC-EA were further optimized and used for the degradation of three typical dye wastewaters, crystal violet (CV), methylene blue (MB) and methyl orange (MO), to investigate their general applicability. The results showed that the optimized Mode CC-EA achieved higher decolorization rates, chemical oxygen demand (COD) and total organic carbon (TOC) removal rates for the four wastewaters, including ARG, than Mode CC within 120 min for the same total input charge. The ACE of Mode CC-EA was on average 1.3 times higher than that of Mode CC, while the EC was on average 25.3% lower. Mode CC-EA achieves efficient use of electrical energy while ensuring the catalytic effect, which is of great application for the efficient treatment of dye wastewater and significance for the industrial development of ECO technology.

Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding

The high energy intensity and rigorous quality demand of injection molding have received significant interest under the background of the soaring production of global plastic industry. As multiple parts can be produced in a multi-cavity mold during one operation cycle, the weight differences of these parts have been demonstrated to reflect their quality performance. In this regard, this study incorporated this fact and developed a generative machine learning-based multi-objective optimization model. Such model can predict the qualification of parts produced under different processing variables and further optimize processing variables of injection molding for minimal energy consumption and weight difference amongst parts in one cycle. Statistical assessment via F₁-score and R² was performed to evaluate the performance of the algorithm. In addition, to validate the effectiveness of our model, we conducted physical experiments to measure the energy profile and weight difference under varying parameter settings. Permutation-based mean square error reduction was adopted to specify the importance of parameters affecting energy consumption and quality of injection molded parts. Optimization results indicated that the processing parameters optimization could reduce ~ 8% energy consumption and ~ 2% weight difference compared with the average operation practices. Maximum speed and first-stage speed were identified as the dominating factors affecting quality performance and energy consumption, respectively. This study could contribute to the quality assurance of injection molded parts and facilitate energy efficient and sustainable plastic manufacturing.

Economic growth and carbon emissions in Pakistan: the effects of China's Logistics Industry

The logistics business is a crucial contributor to economic development, yet it is also the leading source of carbon emissions. Economic growth at the expense of environmental deterioration is a challenging issue; this phenomenon offered a new avenue for scholars and policymakers to investigate and address these issues. The recent study is one of the attempts to explore this intricate subject. The goal of this research is to determine whether or not the Chinese logistics sector has an impact on Pakistan's GDP and carbon emissions as a result of CPEC. The research utilized data from 2007Q1 to 2021Q4 using the ARDL approach for an empirical estimate. Due to the mixed order of variable integration and finite data set, the ARDL technique is well deserved, which helps reach sound policy inferences. The study's key results indicated that China's logistic business enhances Pakistan's economic development and carbon emissions in the short and long term. Similarly, China's energy usage, technology, and transportation contribute to Pakistan's economic progress at the price of environmental damage. The empirical study may be a model for other developing nations, given Pakistan's viewpoint. With the support of the empirical results, policymakers in Pakistan and other associated countries would be able to plan for sustainable growth in conjunction with CPEC.