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The green innovations, environmental policies, and carbon taxes are the tools to achieve sustainable development goals (SDGs) in the mitigation process. This study is intended to examine the impact of innovation, carbon pricing (CTAX), environmental policies (EP), and energy consumption (ECON) on PM2.5 and greenhouse gas (GHG) emission for Central-Eastern European countries. The panel effect during 2000-2018 is tested using a dynamic panel data model while the Granger causality approach obtains country-related outcomes. The outcomes reveal that eco-friendly innovations have a more profound effect on carbon mitigation. Environmental policies reduce emissions by 2.7% in the short run and 17.4% in the long run. Similarly, CTAX mitigates GHG emissions by 8.6% in the short-run and PM2.5 by 0.9% and 5.7% in the short and long run. However, urbanization, energy consumption and trade openness are the leading polluters in the region. The main findings remain dominant in the country-specific results and find unidirectional and bidirectional causality evidence among variables. The research concludes that green innovations and strict environmental policy can lead towards achieving sustainable development goals using carbon taxes as a tool on the way.

This study uses an incentive-compatible experimental online supermarket to assess whether prior environmentally-friendly behaviour outside the store and carbon taxes motivate sustainable consumption. Previous research suggests that past decisions may influence current decisions because consumers compensate morally desirable and undesirable acts over time, and carbon taxes have been promoted as effective tools to reduce the carbon footprint of food baskets. After controlling for past consumption, results show that being required to recall past environmentally-friendly behaviour before shopping led consumers to purchase more sustainable food baskets. Carbon taxation also strongly reduces the carbon footprint of food baskets, showing no interaction with the task of recalling past behaviours.

Great expectations are placed in carbon capture, utilization, and storage (CCUS) technology to achieve the goal of carbon neutrality. Governments adopt carbon tax policies to discourage manufacturing that is not eco-friendly, and subsidies to encourage low-carbon production methods. This research investigates which carbon reduction incentive policy is more viable for the supply chain under CCUS application. The most significant finding is that carbon tax and low-carbon subsidy policies are applicable to high-pollution and low-pollution supply chains with the goal of maximizing social welfare. Both policies play a significant role in reducing carbon emissions. However, it is very important for the government to set reasonable policy parameters. Specifically, carbon tax and low-carbon subsidy values should be set in the intermediate level rather than being too large or too small to achieve higher social welfare. We also find that the higher the value of carbon dioxide (CO2) in CCUS projects, the higher the economic performance and social welfare, but the lower the environmental efficiency. Governments should properly regulate the value of CO2 after weighing economic performance, environmental efficiency and social welfare. The findings yield useful insights into the industry-wise design of carbon emission reduction policies for CCUS and similar projects.

In this paper, a new two-stage optimization framework is proposed to determine the optimal-mix integration of dispatchable Distributed Generation (DG), in power distribution networks, in order to maximize various techno-economic and social benefits simultaneously. The proposed framework incorporates some of the newly introduced regulatory policies to facilitate low carbon networks. A modified Taguchi Method (TM), in combination with a node priority list, is proposed to solve the problem in a minimum number of experiments. Nevertheless, the standard TM is computationally fast but has some inherent tendencies of local trapping and usually converges to suboptimal solutions. Therefore, two modifications are suggested. A roulette wheel selection criterion is applied on priority list to select the most promising DG nodes and then modified TM determines the optimal DG sizes at these nodes. The proposed approach is implemented on two standard test distribution systems of 33 and 118 buses. To validate the suggested improvements, various algorithm performance parameters such as convergence characteristic, best and worst fitness values, and standard deviation are compared with existing variants of TM, and improved genetic algorithm. The comparison shows that the suggested corrections significantly improve the robustness and global searching ability of TM, even compared to meta-heuristic methods.

Great Britain’s carbon emissions from electricity generation fell by two-thirds between 2012 and 2019, providing an important example for other nations. This rapid transition was driven by a complex interplay of policies and events: investment in renewable generation, closure of coal power stations, raising carbon prices and energy efficiency measures. Previous studies of the impact of these simultaneous individual measures miss their interactions with each other and with exogenous changes in fuel prices and the weather. Here we use Shapley values, a concept from cooperative game theory, to disentangle these and precisely attribute outcomes (CO2 saved, changes to electricity prices and fossil fuel consumption) to individual drivers. We find the effectiveness of each driver remained stable despite the transformation seen over the 7 years we study. The four main drivers each saved 19–29 MtCO2 per year in 2019, reinforcing the view that there is no ‘silver bullet’, and a multi-faceted approach to deep decarbonisation is essential.

We investigate the direct role of technological innovation and other factors influencing industrial energy intensity across 17 EU countries over 1995-2009. We develop an innovative industry-level patent dataset and find compelling evidence that patent stock negatively influences industrial energy intensity. In particular, we find a much stronger effect of patent stock on energy-intensive industries with an estimated coefficient of -0.138 which almost double that of less energy-intensive industries (estimated at -0.085). While our results show that energy price remains the major determinant of energy intensity, the chemicals industry, which is not covered by the EU Emissions Trading Scheme (ETS) during the sample period, appears more susceptible to energy prices relative to other energy-intensive industries that are covered by the EU ETS. Exploring regional differences in carbon taxation, we find a significant decline in energy intensity in Northern Europe owing to the carbon tax policy implemented in the early 1990s across the Nordic countries.

AbstractIn this paper, we investigate the economic and environmental impacts of tariffs on carbon embodied in trade. We find that carbon tariffs do reduce foreign emissions, but their ability to improve global cost‐effectiveness of unilateral climate policy is quite limited – even if tariff rates are based on more sophisticated second‐best considerations. If carbon tariffs are levied on the full carbon content of traded goods, they can even increase rather than decrease the global cost of emission reduction. The main effect of carbon tariffs is to shift the economic burden of developed‐world climate policies to the developing world.