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The paper aims at investigation of the nexus between Industry 4.0 (I4.0) and environmental sustainability in addition to exploring the long-run and short-run effects of Industry 4.0 on CO 2 emissions. For this end, energy consumption, internet and communication technology (ICT) exports, research and development (R&D), artificial intelligence (AI), ICT technology patents and bitcoin are taken as control variables of Industry 4.0 for a panel of 9 countries that contribute to 62% of the total CO 2 emissions in the world. For this purpose, the paper follows two approaches. First, the paper proposes utilization of AI and ICT technology patents, technology-related R&Ds and ICT exports as variables of I4.0 in addition to investigating the effects of economic growth, energy consumption and bitcoin. Second, to control structural changes and nonlinearity in the cointegrating relations and existence of degenerate cointegration, Fourier panel bootstrap autoregressive distributed lag model (FPBARDL) is utilized. Afterwards, FPBARDL based long, short and strong causality analyses are conducted. The empirical findings revealed positive impacts of all I4.0-related variables on emissions in the long-run. Though I4.0 related AI and ICT innovation has no direct effect in the short-run, its effects are determined through increased energy consumption towards emissions. The strong positive effects of energy consumption and positive effects of economic growth, ICT exports and R&D are observed both in the short and long-run. In addition to positive impacts of I4.0 on environment, the findings favor insufficiency of policies focusing on lowering emissions in I4.0 context.Policy recommendations include strong commitment to energy efficiency and renewable energies and trade policies with environmental concerns. [ABSTRACT FROM AUTHOR]

There is no one-size-fits-all solution for global energy sustainability. It is affected by a country's policies, resources, financing opportunities, the attention given to it, and the degree to which other pressing issues, such as poverty and access to power, are addressed in each country. This paper used 28 indicators to assess a country's energy sustainability. These indicators ranged from CO 2 per capita emissions to the share of electricity production from wind. The indicators were adjusted so that the lower values were more sustainable. 31 countries were selected. Kohonen's self-organizing maps were used to present this 28-dimensional model in a 2D map. This algorithm is a type of artificial neural network trained by unsupervised learning. Eight clusters of countries were found through the analysis using the four-light traffic panel, where green is the most sustainable, followed by blue, yellow, and red. The closeness on the map does not guarantee that two countries will be in the same cluster. Three color matches and closeness were the criteria for the creation of clusters. The energy transition from the map's lower to upper sides is plainly visible. Red clusters make up the lower side, while green clusters make up the top. One blue cluster and two yellow clusters are located between them. The algorithm converged in the 133rd iteration; the quantization error reached 0 and the topological error reached a value of 25. This method can be used to visualize how far or close a country is to the best achievers. It shows which parameters can or should be improved and what the world trend is. If there is a trend, cost-effective solutions are frequently present. [ABSTRACT FROM AUTHOR]

This paper examines the asymmetric and long-run effect of energy productivity on the quality of the environment in Finland. Finland, as one of the Nordic countries, is a major contributor to the global drive to achieve decarbonization by 2050. In effect, this study seeks to contribute to the strides by querying the extent to which shocks in energy productivity make or mar the sustainability of the Finland environment from 1990:Q1 to 2019:Q4. The other impacts of financial development, economic growth, and trade openness are considered in the model estimated by using the novel hidden panel cointegration and nonlinear autoregressive distributed lag (NARDL) methodology. The results reveal a significant asymmetric equilibrium cointegration relationship among the variables over long-run time horizons; energy productivity utilization enhances the quality of the environment in Finland over the long run while trade openness, financial development, and economic growth contribute to environmental degradation in Finland in the long term. In particular, a 1% positive and negative fluctuation in energy productivity lowers carbon emissions by 1.79442% and 0.84799% in the long term. Whereas 1% change in trade openness, financial development, and economic growth trigger CO 2 emissions (CO 2 E) by 1.15298%, 0.271365%, and 2.93402% in Finland's economies. The results of the frequency domain causality model corroborate the NARDL results. On the basis of these empirical findings, this study suggests that policymakers in Finland should consider the asymmetric behavior among these variables in setting their trade, environmental, growth, and energy policies. [Display omitted] • The effect of energy productivity on environmental degradation in Finland is examined. • Nonlinear ARDL, Fourier ADL cointegration and frequnecy domain causality tests are employed. • Energy productivity leads to lower environmental degradation in Finland. • Energy productivity causes environmental sustainability at different frequencies. [ABSTRACT FROM AUTHOR]

Biofuel manufacturing from renewable biomass through a smart manufacturing system is an important alternative to fossil fuels, which helps to decrease dependability on conventional fuel and decrease carbon emanations. By utilizing efficient labors, smart machines, and minimized energy utilization, the conventional biofuel manufacturing framework can be converted into a smart sustainable manufacturing framework. The existing conventional fuel demand can be replaced by biofuel. This study efforts to make pure biofuel with less amount of carbon emanations and energy utilization through a smart multi-type biofuel manufacturing framework, where the demand of the biofuel is selling price dependent. The different energy costs including air handling cost, lighting cost are calculated in this work-in-process inventory. The carbon emissions cost is included in every stage of this model. A variable demand has introduced for the maximization of profit. To reduce energy consumption and carbon emissions, a two-stage inspection cost with a variable manufacturing rate is taken to make the manufacturing process flexible such that the amount of impure biofuel is minimized. Although a random manufacturing rate is applied through a smart manufacturing system, still impure biofuel is manufactured. The impure biofuel is remanufactured again through refining just after the well-planned manufacturing ends. In this model, the multi-delivery technique is used such that a fixed amount of biofuel of n segments of the pure biofuel is transported to market places at predetermined intervals at the time of delivery frame. The classical optimization technique is used for continuous and differentiable variables and the mixed integer programming technique is utilized for discrete variables to maximize the total profit globally. To validate the model's usefulness, four numerical observations are studied. It is shown that by utilizing the said procedure, the percentage of impure biofuel can be decreased truly through the minimized energy consumption. The graphical representation and the sensitivity experiment show the impact of every parameter with the whole cost of the research paper. Numerical results assist to get the maximum profit and the optimum selling price of biofuel globally in a sustainable smart multi-type biofuel manufacturing system. [Display omitted] [ABSTRACT FROM AUTHOR]

Advance cooling/lubrication (lubricooling) approaches are getting fame in industry and academia due to their excellent performance. A significant amount of work has been conducted to investigate the effect of various lubricooling approaches on the machinability of the hardened steel. However, sustainability-based performance evaluation of lubricooling assisted machining is missing. Thus, this paper presents 3E-based (Energy, Economics, Environment) analysis to compare the performance of various lubricooling approaches. Initially, preliminary experiments were performed to achieve optimal flow rates of various cutting fluids used in Minimum Quantity Lubrication (MQL), cryogenic cooling, and conventional emulsion (Flood) approaches. Later, optimal flow rates were used to achieve equal cutting-tool life under all machining environments. Results showed that with the same tool life method (STLM), Cryogenic-MQL(CryoMQL) could withstand aggressive cutting speeds and produce the highest productivity among all lubricooling approaches. In addition, it yielded 50% lower-priced specific production cost as compared to dry machining. However, 44.3% more specific-CO 2 emitted in CryoMQL as compared with dry machining. In summary, the hybrid CryoMQL assisted machining process is sustainable economically but not environmentally. The outcomes of the present study provide useful information, and it can help machinist to enhance the process performance. [Display omitted] • Holistic sustainability assessment of hard machining. • New holistic models for energy consumption, CO 2 emission, and cost. • Determining optimal flow rate of cooling/lubrication approaches. • Comparison of energy-conscious advanced lubricooling technologies. • Proposed CryoMQL yielded 50% lower price products. [ABSTRACT FROM AUTHOR]

Increasing energy demand and the rising levels of greenhouse gas emissions have increased the necessity of bioenergy and sustainable energy generation. The sustainable bioenergy supply chain is the key to produce sustainable biofuel. This paper points to render an optimization model for planning a versatile and dependable biomass-to-bioenergy sustainable supply chain network in which crop residuals from several agricultural sectors, multi-transportation disruption modes, multi-biorefineries, multi-biogas plants, and multi-market centers are investigated for two bioenergy sources, namely biofuel and biogas. For this reason, supply chain cost optimization, emissions of feedstock transport, processing, and distributing end biofuel to respective markets are utilized. The multi-setup-multi-delivery (MSMD) concept is incorporated and lead time crashing cost is applied to minimize the lead time in this model. The carbon emission costs at all steps and the variable production rate for the production of bioenergy are included within the model. Finally, one small-scale data and another large-scale data of six agricultural areas, four biorefineries, four biogas plants, and six markets are considered to validate the model's usefulness. The model's goal is to show bioenergy's effect to make a sustainable supply chain of biofuel and biogas. The numerical results reveal that the cost of bioenergy production contributed 54.12%, a major extent of the whole cost in biorefinery and biogas plants. The transportation segment is the preeminent initiator of carbon emissions with 83.33% in the entire carbon emission within the whole supply chain. Although, transportation cost contributed 33.09% of the total cost, the multi setup-delivery-multi-delivery policy minimized the whole supply chain cost. The results provide a systematic guideline for developing a sustainable biofuel supply-chain by minimizing cost, various aspects of transportation logistics, and multi-model alternatives under reduced energy effects. • Energy demand and two approaches of biofuel and biogas production are evaluated. • Reduction of the supply chain network's total cost. • Optimizing allocation of the appropriate quantity of residual biomass and biofuel. • Reducing energy consumption by using variable production rate. • An intelligent logistics system with multi-disruption is addressed. [ABSTRACT FROM AUTHOR]