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Oil is crucial for industrial development. This paper investigates the impacts of oil price changes on China's industrial growth and examines whether the impacts are asymmetric. The estimations can help determine how oil price shocks are transmitted throughout the economy.

This paper adopts West Texas Intermediate (WTI) crude oil price and industrial sector output and uses monthly data. The recently developed nonlinear autoregressive distributed lag (NARDL) model is employed to illustrate the effects in both the short term and long term. Importantly, under NARDL framework, this paper examines whether the impacts are asymmetric by decomposing oil price shocks into their positive and negative partial sums.

The empirical results prove clear evidence of asymmetries in the short term, long term or both terms. Specifically, some sectors benefit from, rather than suffer from higher oil prices, even some energy-intensive sectors, i.e. C31 (Smelting and Pressing of Ferrous Metals) and C32 (Smelting and Pressing of Non-ferrous Metals). However, the effects on some other energy-intensive sectors appear insignificant. Additionally, the results prove significantly negative responses in some sectors in the long term, and most of these sectors are in the top half of the ranking by energy consumptions.

This paper studies the economic responses at a disaggregated level by employing industry-level data. NARDL method is used to decompose oil price changes into their increases and decreases and investigate the asymmetries in the impacts of oil price changes.

How do energy consumption, efficiency and economic performance vary between small enterprises belonging to two different product clusters whose production process and technologies differ?

The objectives are analyzed based on empirical data gathered from a field survey of small enterprises with reference to auto ancillaries in Shimoga and brick‐making enterprises in Malur of Karnataka State in India. Simple averages, correlation and multiple‐regression techniques are used for the analysis.

The study brought out that higher energy intensity results in higher share of energy cost in total variable cost. Energy intensity had a negative relationship with value of output. Energy makes a statistically significant contribution to returns to scale. The classification of small enterprises into two groups based on above average energy intensity and below average energy intensity, and the subsequent regression analysis brought out that energy intensity had a positive influence on returns to scale in auto ancillaries whereas a negative influence on returns to scale in bricks enterprises.

The sample‐size formulation could not be done on a scientific basis due to the absence of comprehensive data on all small enterprises operating in the respective clusters and therefore, the findings may not be generalized.

Industry specific characteristics must be taken into account while introducing “energy efficiency improvement” programmes as a means of enhancing competitiveness in “energy intensive” small enterprises.

The paper illustrates the scope for energy conservation and efficiency improvement in Indian small enterprises.

This paper aims to measure the state-level energy efficiency in Indian paper industry and simultaneously explain inter-state variation in efficiency by inefficiency effect model. Three variables, labor productivity, capital intensity and structure of paper industry, are included in inefficiency effect model to assess the likely impact on energy efficiency.

Sub-vector input distance function is derived through neo-classical production function which provides measures to estimate energy efficiency. Assuming a translog production function specification, energy efficiency is estimated by using Battese and Coelli (1995) stochastic frontier analysis (SFA). The authors also estimated four other SFA models, and energy efficiency from all the models is compared for robustness checking.

The results show the existence of a vast potential to improve energy efficiency. Inefficiency effect model reported a positive impact of structure of the industry and capital intensity on energy efficiency performance, while labor productivity does not have any significant impact on energy efficiency. There exists considerable energy efficiency variation among states. Uttarakhand, Punjab and Orissa are the best performing states while Rajasthan, Jharkhand and Goa have worst energy efficiency performance based on average efficiency. The ranks assigned to states according to inefficiency effects model are found contrary to the simple measure of energy efficiency, i.e. energy intensity. Thus, energy intensity may not always be a good proxy for underlying energy efficiency and need to be compared with a comprehensive possible measure.

To the best of the authors’ knowledge, this is the first study which measures energy efficiency of Indian paper industry through stochastic frontier model using region-level data. Instead of relying on traditional energy efficiency indicators (energy-output ratio), total-factor energy efficiency approach is used to conduct the empirical exercise. Deviations from the frontier because of factors beyond the scope of producers are also incorporated into analysis to portray the magnitude of random factors in influencing the efficiency performance.

Indonesia is the fourth most populous country in the world, which has a strong effect on primary energy use and depletion of natural resources. This paper considers energy intensity (EI) defined as a measure of the amount of energy it takes to produce a dollar's worth of economic output. The purpose of the paper is to explore how different factors contributed to the decline in Indonesia's EI.

The cointegration regression methodology is applied to explore the long-term nexus between EI and its factors in Indonesia during 1990–2016.

Results show that domestic credit to the private sector, as well as the share of alternative energy, has a significant impact on the decline of EI in Indonesia.

We do not try to rule out other possible determinants of EI. We consider the determinants of EI using time series data, while an ideal analysis would be based on panel-level data. Another limitation is that the study covers only the small-time period from 1990 to 2016.

Our findings serve to aid the government and policymakers in prioritizing improvements in the sphere of energy policy. An important policy implication, regarding Indonesia, that arises from our study is that, for the country to be able to decrease its EI, it must be able to develop its financial market and zero-carbon energy sources, mainly geothermal energy with its huge potential.

We show that energy prices, financial development and the share of alternative energy sources contribute to EI decrease. Policy recommendations include geothermal and solar energy development as one of the most prospective sources of alternative energy in Indonesia.

Carbon dioxide emission is one of the key causes of global warming and climate change. This study investigates the effects of domestic credit and manufacturing indicators on the emission of carbon dioxide in South Africa.

The paper relied on time series data from 1975 to 2014 and employed regression and variance decomposition methods to analyze the data.

In the long run, manufacturing output increases total carbon emissions and emissions from solid fuel; manufactures trade reduces carbon emissions and domestic credit reduces emissions from the manufacturing industries and construction. The long-run effect of the changing technical characteristics of the manufacturing sector is sensitive to the estimation technique used. In the short run, however, changing technical characteristics of the manufacturing sector affect the level of carbon emissions. Income increases emissions from manufacturing industries and construction and urbanization increases total carbon emissions.

Policymakers have to initiate effective policies to promote energy-efficient technologies among manufacturing firms.

The paper examines the effect of manufacturing on carbon dioxide emissions in South Africa. It also examines the possible effect of manufactures trade on carbon emissions. Moreover, the possible effect of the changing characteristics of the manufacturing sector on carbon emissions is investigated.

The purpose of this paper is to evaluate different greenhouse gas (GHG) mitigation policy instruments implemented in Slovenia, especially their impact on industrial competitiveness.

Analysis of existing mitigation policies.

The introduction of new policy instruments in Slovenia has not been very effective in curbing GHG emissions, but it certainly brought attention to the problem. As there is still a lot of space for improvements, additional effort should be made to improve existing instruments or to propose additional mitigation measures.

The paper evaluates existing policy instruments, which are still in their evolutional phases. The significance of this paper is to help to intensify indirect influence on GHG emissions reduction, especially on the national level, as proper introduction and understanding of the problem leads to more comprehensive and credible solutions regarding GHG emission reduction strategies. In addition, some new steps and/or measures are also indicated with this paper, especially concerning future evolution of EU emission trading scheme and national CO₂ tax regulation.

The paper is a new source of information about implementation of GHG mitigation policy measures in Slovenia. Analysis of adaptation and mitigation activities as well as integration of all aspects of climate change issues into strategies for sustainable development is of significant importance for the relevant decision makers – to monitor the impact of their own policies for domestic assessment purposes, that is to choose a policy strategy, to understand the implications of existing and alternative policy strategies, and to understand the joint interactions of multiple, individual policy strategies.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting‐edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

the paper finds that climate change and greenhouse gas emissions mitigation policies are environmental issues of significant importance. The greenhouse effect is a natural phenomenon that helps to regulate the temperature of our planet, however, through our interference with the carbon cycle (burning fossil fuels), we artificially move carbon from solid storage to its gaseous state, thereby increasing atmospheric concentrations and consequently intensifying the greenhouse effect, which warms our planet.

The paper provides strategic insights and practical thinking that have influenced some of the world's leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy‐to‐digest format.

The authors target the interrelationships between non-fungible tokens (NFTs), decentralized finance (DeFi) and carbon allowances (CA) markets during 2021–2023. The recent shift of crypto and DeFi miners from China (the People's Republic of China, PRC) green hydro energy to dirty fuel energies elsewhere induces investments in carbon offsetting instruments; this is a backdrop to the authors’ investigation.

The quantile vector autoregression (VAR) approach is employed to examine extreme-quantile-connectedness and spillovers among the NFT Index (NFTI), DeFi Pulse Index (DPI), KraneShares Global Carbon Strategy ETF price (KRBN) and the Solactive Carbon Emission Allowances Rolling Futures Total Return Index (SOLCARBT).

At bull markets, DPI is the only consistent net shock transmitter as NFTI transmits innovations only at the most extreme quantile. At bear markets, KRBN and SOLCARBT are net shock transmitters, while NFTI is the only consistent net shock receiver. The receiver-transmitter roles change as a function of the market conditions. The increases in the relative tail dependence correspond to the stress events, which make systemic connectedness augment, turning market-specific idiosyncratic considerations less relevant.

The shift of digital asset miners from the PRC has resulted in excessive fuel energy consumption and aggravated environmental consequences regarding NFTs and DeFi mining. Although there exist numerous studies dedicated to CA trading and its role in carbon print reduction, the direct nexus between NFT, DeFi and CA has never been addressed in the literature. The originality of the authors’ research consists in bridging this void. Results are valuable for portfolio managers in bull and bear markets, as the authors show that connectedness is more intense under such conditions.

Despite the success in achieving the objectives for the use of renewable energy sources, the EU’s competitiveness is not at the desired level. In particular, the largest decreases in fossil-type energy intensity were observed in last 13 members of EU, namely, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia and Slovenia. The purpose of this paper is to trace how these countries protect the competitiveness of their dirty (energy-intensive) industries.

The study employs revealed comparative advantage (RCA) indices to measure the comparative advantage (CA) of EU-13 in dirty industries for the period 1995-2014 and assesses these indices in the framework of EU’s climate policy.

Some policies which make industries to adapt EU’s 20-20-20 targets are forcing industries. In order to compete, these industries are leaving Europe and looking elsewhere. In this study the authors found that, particularly chemicals and non-metallic mineral manufactures resulted in a weakening of their CA over the years in some of these members. Similarly it is found that the RCA indices of iron and steel and non-ferrous metals are decreasing.

The study addresses the EU-13’s position in terms of their competitiveness and find the connection with the EU’s climate policy through their RCAs of dirty industries.

Based on self-determination theory, the paper investigates the link between environmentally specific transformational leadership (ETFL) and subjective well-being (SWB) of employees working in an energy intensive industry. The paper also aims to examine pro-environmental behavior (PEB) as a mediator mechanism between ETFL and SWB relationship.

An empirical research design was used for the analysis. In total, 293 respondents working in different energy intensive Indian companies filled the questionnaire for the study. The data were analyzed using structural equation modeling SMART-PLS 3 software which is a graphical user interface for variance-based structural equation modeling using the partial least squares path modeling method. Two stages of analysis were conducted: the measurement model and the structural model.

The results indicate that (1) ETFL has a significant positive impact on PEB. (2) PEB is positively correlated to SWB. (3) ETFL has a significant impact on SWB. (4) The underlying mechanism process of ETFL and SWB relationship is explained by mediation of PEB.

Overall, the study insinuates that PEB act as underlying mechanism in environmental leadership and SWB of employees. The study further suggests that the management should promote green leaders for enhancing the overall SWB of the managements' employees especially in energy intensive sector. Doing so helps the managers to enhance managers' environmental conscious behavior for inducing SWB of employees.

The study contributes to novel findings in existing literature particularly to determine ETFL and PEB as predictors of SWB especially in energy intensive sector. The study also leads the ways for organizations by promoting green conscious leaders, which stimulates PEB among employees that ultimately contributes to SWB of employees. Furthermore, the research methodology can be used in other studies for variation of contextual variables such as size of the industry, developed or underdeveloped country and manufacturing or service industry.