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Abstract Cities do play significant roles in tackling global environmental changes. Decoupling economic growth from environmental pressures is critical for global sustainable development, however, few studies pay attention to the decoupling analysis at city level. This paper conducts a comparative study of the decoupling performance of economic output from CO 2 emissions between Beijing and Shanghai, the top two cities in China. In this paper, the decoupling trends and the decoupling effects are explored from a sectoral perspective, by combining the Tapio decoupling elasticity and the logarithmic mean Divisia index (LMDI) model. The results show that Beijing and Shanghai both experienced weak decoupling in construction, expansive negative decoupling in transport, expansive coupling in trade, and weak decoupling in others over the period 2000–2015. Moreover, agriculture appeared strong decoupling in Beijing, and recessive decoupling in Shanghai; industry showed strong decoupling in Beijing, and weak decoupling in Shanghai. The decoupling effects showed different magnitudes and patterns in industrial sectors between Beijing and Shanghai. Overall, per-capita GDP and population growth inhibited the decoupling, while energy intensity, industrial share, energy mix accelerated the decoupling process. Highlights • The decoupling of sectoral economic output from carbon emissions in Beijing and Shanghai are investigated. • Beijing's industry enjoyed better decoupling performance than Shanghai's industry. • Transport should be put more emphasis in further decoupling efforts. • Energy intensity is crucial effect accelerating the decoupling process. [ABSTRACT FROM AUTHOR]

The Chinese government committed to reduce its national CO 2 emissions intensity by 40–45% until 2020 in response to climate change. This paper quantitatively evaluates the performance of CO 2 emissions reduction over the period 2005–2020 from the historical and prospective perspectives by using a combination of decomposition analysis and scenario analysis, aiming to provide suggestions about how to achieve the target in key emissions reduction fields. Unlike traditional index decomposition methods, this paper incorporates the total energy conversion efficiency effect into the model, and further decomposes it into a final energy mix effect and a final energy conversion efficiency effect by a multilevel decomposition procedure, allowing to measure the contributions of primary energy structure low-carbonization, the final energy mix electrification, and the energy conversion efficiency improvement. The results show that the carbon intensity could decline over the period 2005–2020 by 47.8%, 50.9%, 48.0%, 44.5% and 47.5% in the Business as Usual, S1, S2, S3 and S4 scenarios, respectively. This shows that the 40–45% target is very likely to be achieved. The final energy intensity effect is always the most important driving factor, causing carbon intensity to decrease by 24.0% and 22.1%, again respectively, from 2005–2012 to 2012–2020. Moreover, the energy conversion effect was another major driver during 2005–2012. The Chinese government needs to make more efforts to adjust the industrial structure, which could cause carbon intensity to decrease by 3.6% during 2012–2020, and to adjust the primary energy mix, which could cause carbon intensity to decrease by 7.1% during 2012–2020. [ABSTRACT FROM AUTHOR]

Alleviating global climate change requires the development and adoption of green innovation. This study compares the impact of cooperative and independent green innovation on carbon reduction based on the panel data covering 252 cities in China from 2003 to 2019. We examine the heterogeneous impacts of cooperative green innovation across units, and explain the transmission mechanism. The two-way fixed effects model and interaction model are adopted. Results show that: (1) Cooperative green innovation can reduce carbon emissions more effectively than independent green innovation. A 1% increase in cooperative green patents (inventions) will reduce carbon emissions by 0.023% (0.037%), while independent green patents or inventions play a weak role. (2) For research and development (R&D) partners, firms' cooperative green innovation has a stronger positive effect on carbon reduction than research organizations. The carbon reduction effect of cooperative green innovation is better for listed firms and universities. (3) Mechanism analysis shows cooperative green innovation can reduce carbon emissions by improving the industrial structure and decreasing energy intensity. These effects are stronger when cities have more talent, funds and higher marketability. (4) We also find that listed firms' cooperative green inventions with a ten-year lag can still reduce carbon emissions effectively. Cooperative green innovation is conducive to carbon reduction among eastern and central cities of China. [Display omitted] • This paper explores the impact of green innovation on carbon reduction from the new perspective of cooperation. • Cooperative green innovation is more effective than independent green innovation in carbon reduction. • Cooperative green innovation of listed firms and universities is more conducive to carbon reduction. • Cooperative green innovation can optimize the industrial structure and decrease energy intensity for better carbon reduction. • Cooperative green innovation with more talent, funds and market has better carbon reduction effect. [ABSTRACT FROM AUTHOR]

Climate change which is induced from energy consumption and CO 2 emissions has got people's attention; hence, many strategies and ways are being adopted to mitigate CO 2 emissions. Energy substitution is crucial to the promotion of energy conservation and CO 2 emissions reduction. The purpose of this paper is to estimate both inter-factor and inter-fuel substitution in China's iron & steel industry during the period 1985–2011. Empirical results show several conclusions for China's iron & steel industry: (1) own-price elasticity of energy is negative, so increase in energy price will reduce energy input; (2) energy and capital, and energy and labor are substitutes, and the substitution in energy/capital is relatively stronger; (3) though coal, oil and electricity are also substitutes, the substitution effect is not so obvious because of pricing distortions in China. The driving forces of energy efficiency change are further discussed and the results show that technology progress is the key factor determining energy intensity. Therefore, market-oriented reforms in terms of energy prices, improvement in energy substitution (including inter-factor and inter-fuel substation) and promotion of energy saving technologies are important means for ensuring future energy conservation and CO 2 emissions reduction practices in China's iron & steel sector. [ABSTRACT FROM AUTHOR]

The 2015 Paris climate change negotiations represents the most ambitious worldwide effort to date to combat climate change. As the largest energy consumer and carbon emitter, China pledges 60–65% cut in carbon intensity below 2005 levels by 2030. To accelerate China's climate efforts, China has also committed to cap its primary energy use at 4.8 billion tons of coal equivalent in 2020. This paper proposes an equitable and efficient scheme to disaggregate this national goal among provinces. A three-step disaggregation procedure is proposed which takes into account the initial allocation, national redistribution and inter-provincial adjustment. First, this study allocates the incremental energy quota, taking 2010 as the base year. Second, part of the energy quota is set aside by the central government for redistribution purposes, while an incremental energy quota index comprising equity and efficiency criteria is used for initial allocation among provinces. Finally, historical efforts and inter-provincial energy flows are considered for inter-provincial adjustment. This disaggregation scheme is flexible and can be easily extended to allocation and management of other resource and environmental targets. The empirical findings can assist decision-makers in identifying which provinces are likely to over-consume the energy quota relative to the proposed allocation and which provinces are likely to perform better in energy conservation. Moreover, the results show that the energy cap set for 2020 is unachievable without further action. Market-based instruments, such as an energy allowance trading market, are strongly recommended to achieve the energy use target in an equitable and cost-effective manner. [ABSTRACT FROM AUTHOR]

In a joint U.S.–China statement on climate change , the Chinese government declared to peak CO 2 emissions around 2030. This demonstrates the government's determination to deal with carbon emission and climate change. Although the food industry is not a carbon emission-intensive industry, its large scale makes the emission reduction in the industry very important. Based on the input–output structural decomposition method, this paper calculates the CO 2 emissions of China's food industry from 1991 to 2012, and decomposes the change in CO 2 emissions of the industry during 1992–1997, 1997–2002, 2002–2007 and 2007–2010 into four main effects: emission factor, energy structure, energy intensity, and total output (including four sub-effects: intermediate use, domestic final demand, import substitution and export extension). The results show that changes in CO 2 emissions in the food industry mainly depends on total output effect and energy intensity effect. Energy intensity effect is the most important factor reducing CO 2 emissions, as it reduced cumulative 213 million tons (Mt) CO 2 emissions of the industry. Among total output effect, the effects of intermediate use and domestic final demand are the two biggest contributors to carbon emissions. Finally, we provide some policy advices for constraining and reducing CO 2 emissions of China's food industry. The advices include increasing R&D investment and the substitution of energy with other input factors to decrease energy intensity, increasing the added value of the food industry, optimizing the energy structure with more clean and low-carbon energy, and maintaining the prices of raw materials of the food industry (i.e. agricultural products) with taxes or subsidies. [ABSTRACT FROM AUTHOR]