Long-term Rebound Effects Research Articles

The impact of behavior safety management system on coal mine work safety: A system dynamics model of quadripartite evolutionary game

Employees’ unsafe behavior is the main cause of coal mine accidents, and an effective and efficient coal mine behavior safety management (CMBSM) system is critical to ensuring coal mine work safety. To explore the impact of CMBSM system on coal mine work safety, a four-player evolutionary game model of the CMBSM system including organizations, managers, coal miners, and groups is developed. Then, the behavior strategy interactions and evolution processes of stakeholders in the CMBSM system are analyzed in combination with system dynamics modeling. Moreover, the impacts of key parameters on the decision-making behavior of stakeholders are analyzed based on the distribution proportion of rewards and penalties in the context of organization incentives applied for Chinese coal mines. The results are as follows. First, the initial strategy combinations affect the overall stability of the CMBSM system. Second, the quadripartite evolutionary game applied for the CMBSM system has a long-term rebound effect under the static reward proportion strategy. Then, the positive incentive behaviors of organizations cannot be maintained for a long time under the static penalty proportion strategy. Finally, the CMBSM system can evolve to the best stable state under the combined dynamic reward and dynamic penalty proportion strategies. Some behavior safety management strategies that can improve coal mine work safety are offered based on the results. More importantly, the results confirm the importance of strengthening behavior safety management for coal mine work safety and raise new avenues for safety management in other industries.

A dynamic analysis of industrial energy efficiency and the rebound effect: implications for carbon emissions and sustainability

Energy efficiency improvement (EEI) is generally known to be a cost-effective measure for meeting energy, climate, and sustainable growth targets. Unfortunately, behavioral responses to such improvements (called energy rebound effects) may reduce the expected savings in energy and emissions from EEI. Hence, the size of this effect should be considered to help design efficient energy and climate targets. Currently, there are significant differences in approaches for measuring the rebound effect. Here, we used a two-step procedure to measure both short- and long-term energy rebound effects in the Swedish manufacturing industry. In the first step, we used data envelopment analysis (DEA) to measure energy efficiency. In the second step, we use the efficiency scores and estimated a derived energy demand equation including rebound effects using a dynamic panel regression model. This approach was applied to a firm-level panel dataset covering 14 sectors in Swedish manufacturing over the period 1997–2008. We showed that, in the short run, partial and statistically significant rebound effects exist within all manufacturing sectors, meaning that the rebound effect decreased the energy and emission savings expected from EEI. The long-term rebound effect was in general smaller than the short-term effect, implying that within each sector, energy and emission savings due to EEI are larger in the long run compared to the short run. Using our estimates of energy efficiency and rebound effect, we further performed a post-estimation analysis to provide a guide to policy makers by identifying sectors where EEI have the most potential to promote sustainable economic growth with the lowest environmental impact.

Design of energy-conservation and emission-reduction plans of China’s industry: Evidence from three typical industries

It is important to estimate the energy-conservation and emission-reduction potentials accurately and design practicable paths to ensure the achievement of the emission reduction target set at the “Paris Climate Conference”. Selecting the petroleum and gas extraction industry, the construction industry, and the transportation industry, which are the greatest energy consumers and carbon emitters in China, this study estimates their energy-conservation and emission-reduction potentials of 2018–2025 and designs the implementation paths. The results show that: (1) under the short-term energy rebound effect, the cumulative energy-conservations of 2018–2025 of the three industriesare17.55%, 18.29%, and 13.46%, respectively; the cumulative emission-reductions are 18.21%, 14.85%, and 16.98%, respectively. Under the long-term rebound effect, the cumulative energy-conservations are 21.3%, 22.39%, and 18.54%, respectively; the cumulative emission-reductions are 19.76%, 18.1%, and 23.75%, respectively. Moreover, the effect of the short-term energy rebound effect is greater than the long-term rebound effect. (2) There are three paths for energy-conservation and emission-reduction: the “unilateral breakthrough”, the “promotion of advantages and compensate for disadvantages”, and the “leap forward” paths. (3) When formulating the energy-conservation and emission-reduction policies, for industries of resource-intensive, factor market distortion, governments’ administrative orders and intensive production should be focused on; for industries of labor-intensive, energy efficiency, energy structure, carbon tax and the upgrade of industry structure should be focused on; for industries of capital-intensive, regional investment bias, the construction of carbon trading system and clean production should be focused on. Our results can provide guidance for the formulation of energy-conservation and emissions-reduction objectives and policies for China’s industry.

A Dynamic Analysis of Industrial Energy Efficiency and the Rebound Effect

Energy efficiency improvement (EEI) is generally known to be a cost-effective measure for meeting energy, climate and sustainable growth targets. Unfortunately, behavioral responses to such improvements (called energy rebound effects) may reduce the expected savings in energy and emissions from EEI. Hence, the size of this effect should be considered to help set realistic energy and climate targets. Currently there are significant differences in approaches for measuring rebound effect. Here, we used a two-step procedure to measure both short- and long-term energy rebound effects in the Swedish manufacturing industry. In the first step, we used data envelopment analysis (DEA) to obtain energy efficiency scores. In the second step, we estimated energy rebound effects using a dynamic panel regression model. This approach was applied to a firm-level panel dataset covering all 14 sectors in the Swedish manufacturing industry over the period 1997–2008. We showed that, in the short run, partial rebound effects exist within most of manufacturing sectors, meaning that the rebound effect decreased, but did not totally offset, the energy and emission savings expected from EEI. The long-term rebound effect was smaller than the short-term effect, implying that within each sector, energy and emission savings due to EEI are larger in the long run compared to the short run.

Evaluation of the development of biobutanol with reference to continental level: The rebound effect and effectiveness of the Paris Agreement

This work modifies the dynamic GTAP model by incorporating the production and sales structure of the biobutanol industry to forecast the path of the development of biobutanol by continent. The results reveal that developed countries should focus on biobutanol technological advances and developing countries should concentrate increasing the flexibility of the structure of biobutanol industry for the development of bioenergy. The production of biobutanol is highly sensitive to the elasticities of energy substitutes.The long-term rebound effects on biobutanol will range from 58.2% to 89.6%. Developed continents are more energy-efficient. Improving energy efficiency increases production of production. Investment in the early stage of a new bioenergy industry will increase the value of the production of bioenergy.The Paris Agreement without green investment may reduce industrial energy production except in South America and parts of North America. The IEA's Bridge Strategy is effective in keeping global warming to less than 2 °C, but will decrease economic growth globally. This study provides useful information for biobutanol investors and decision makers in the field of who are affected by climate change.

Direct rebound effect on urban residential electricity use: An empirical study in China

Though improving energy efficiency is an important approach to decrease the energy consumption, the rebound effect caused by technology progress negatively affects the effectiveness of energy efficiency policies. This paper empirically investigates direct rebound effect of urban residential electricity use in China. Using China's 30 provincial government panel data from1996 to 2010, we build a co-integration equation and a panel error correction model to analyze the direct rebound effect. The results indicate that an obvious rebound effect in the Chinese urban residential electricity consumption does exist. Specifically, the long-term rebound effect is 0.74, while the short-term rebound effect is 0.72. Therefore, the rebound effect significantly impairs functions of energy efficiency policies. For this reason, Chinese government should take the rebound effect into consideration when formulating energy policies.

Traveled distance, stock and fuel efficiency of private vehicles in Canada: price elasticities and rebound effect

This paper presents estimates of the rebound effect and other elasticities for the Canadian light-duty vehicle fleet using panel data at the provincial level from 1990 to 2004. We estimate a simultaneous three-equation model of aggregate demand for vehicle kilometers traveled, vehicle stock and fuel efficiency. Price and income elasticities obtained are broadly consistent with those reported in the literature. Among other results, an increase in the fuel price of 10% would reduce driving by ~2% in the long term and by 1% the average fuel consumption rate. Estimates of the short- and long-term rebound effects are ~8 and 20%, respectively. We also find that an increase in the gross domestic product per capita of 10% would cause an increase in driving distance of 2–3% and an increase of up to 4% in vehicle stock per adult. In terms of policy implications, our results suggest that: (1) the effectiveness of new fuel efficiency standards will be somewhat mitigated by the rebound effect and (2) fuel price increases have limited impacts on gasoline demand.