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Cities are a major source of energy use and greenhouse gases emissions, as well as being at the core of the climate change mitigation. With the Revitalizing Old Industrial Base of Northeast China strategy, Northeast China has been a typical developing region with rapid industrialization and urbanization accompanied by substantial energy consumption and carbon emissions. Therefore, northeastern Chinese cities should play an important role in regional lowcarbon developments. This study presents several improvements to previous method to improve the accuracy of the results. Using the modified method, for the first time, we compile carbon emission inventories for 30 cities in Northeast China based on fossil fuel combustion and industrial processes. The results indicate that Anshan emitted the most carbon emissions annually, followed by Benxi and the vice-provincial cities (including Changchun, Shenyang, Dalian and Harbin). In 2012, the total carbon emissions of the 30 cities amounted to 973.95 million tonnes, accounting for 9.71% and 2.75% of national and global carbon emissions, respectively. Most of the CO2 emissions of these cities were from the 'nonmetal and metal industry' and 'energy production and supply'. Raw coal was the primary source of carbon emissions in Northeast China, and industrial processes also played a significant role in determining the carbon emissions. Additionally, both the average per capita carbon emissions and carbon emission intensity in the 30 cities were higher than the national levels. According to the differences in carbon emissions characteristics, we present several policy recommendations for carbon mitigation for northeastern Chinese cities. This study provides consistent and comparable spatial-temporal city-level emission database for further research on relationships between economic development and environmental protection in Northeast China.Simultaneously, this study provides practical reference values for other developing regions throughout the world to create low-carbon road maps. Highlights: CO2 inventories of 30 cities in Northeast China were compiled for the first time. We presented several improvements to previous method to improve the accuracy. 973.95 million tonnes were emitted in 2012 accounting for 9.71% of national levels. Anshan emitted the most CO2 followed by Benxi and the vice-provincial cities. Both the average per capita CO2 and CO2 intensity were higher than national levels.

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The CO2 emission-mitigation policies adopted in different Chinese cities are important for achieving national emission-mitigation targets. China faces enormous inequalities in terms of regional economic development and urbanization, with some cities growing rapidly, while others are shrinking. This study selects 280 cities in China and divides them into two groups of growing cities and two groups of shrinking cities. This is achieved using an index called “urban development degree,” which is calculated based on economic, demographic, social, and land-use indicators. Then, the 280 cities’ CO2 emission characteristics are examined, and extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) is used to verify the influencing factors. We find that rapidly growing cities (RGCs) present a trend of fluctuating growth in CO2 emissions, rapidly shrinking cities (RSCs) show an inverted U-shaped trend, and slightly growing (SGCs) and slightly shrinking cities (SSCs) show a trend of rising first, followed by steady development. Moreover, for growing cities, the population, economy, and proportion of tertiary industry have positive effects on carbon emissions, while technology has negative effects. For shrinking cities, the population and economy have significant positive effects on carbon emissions, while technology and the proportion of tertiary industry have negative effects.

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Reducing CO2 emissions of industrial energy consumption plays a significant role in achieving the goal of CO2 emissions peak and decreasing total CO2 emissions in northeast China. This study proposed an extended STIRPAT model to predict CO2 emissions peak of industrial energy consumption in Jilin Province under the four scenarios (baseline scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS), and low-carbon scenario (LCS)). We analyze the influences of various factors on the peak time and values of CO2 emissions and explore the reduction path and mechanism to achieve CO2 emissions peak in industrial energy consumption. The results show that the peak time of the four scenarios is respectively 2026, 2030, 2035 and 2043, and the peak values are separately 147.87 million tons, 16.94 million tons, 190.89 million tons and 22.973 million tons. Due to conforming to the general disciplines of industrial development, the result in ELS is selected as the optimal scenario. The impact degrees of various factors on the peak value are listed as industrial CO2 emissions efficiency of energy consumption > industrialized rate > GDP > urbanization rate > industrial energy intensity > the share of renewable energy consumption. But not all factors affect the peak time. Only two factors including industrial clean-coal and low-carbon technology and industrialized rate do effect on the peak time. Clean coal technology, low carbon technology and industrial restructuring have become inevitable choices to peak ahead of time. However, developing clean coal and low-carbon technologies, adjusting the industrial structure, promoting the upgrading of the industrial structure and reducing the growth rate of industrialization can effectively reduce the peak value. Then, the pathway and mechanism to reducing industrial carbon emissions were proposed under different scenarios. The approach and the pathway and mechanism are expected to offer better decision support to targeted carbon emission peak in northeast of China.

The building sector has gradually become a major contributor of carbon emissions in recent years. Its carbon emissions, which result from the long heating period and considerable consumption of coal in residential buildings during operation, must be reduced. To this end, the long-range energy alternatives planning system was adopted for the forecasting of carbon emissions in baseline scenarios, energy-saving, energy-saving–low-carbon, and low-carbon. On the basis of these predictions, the contributions of heating, cooling, cooking, illumination, washing, and other activities to carbon emissions were analyzed. The influencing factors in the reduction of carbon emissions from residential buildings in a cold region were identified. The results showed that energy-saving–low-carbon was the optimal scenario to reduce carbon emissions. Meanwhile, carbon emissions will peak in 2030, with a value of 42.06 Mt under the same scenario. As the top three influencing factors, heating, cooling, and cooking contribute 55.74%, 18.86%, and 17.29% of carbon emissions, respectively. Sensitivity results showed the differential effects of 32 factors on the reduction of carbon emissions in residential buildings. Carbon emissions could be reduced by 17.41%, 35.51%, 31.10%, and 14.10% by controlling the building scale, heating, cooling, and cooking, respectively. To this end, seven factors, including the rationing of central heating, were identified. Then, pathways to reducing carbon emissions were proposed under different scenarios. The present research fills the gap between reality and the predicted pathway, considering the heterogeneity of the climate.

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