Since the initiation of reform and opening up policy in the late 1970s, agriculture has developed rapidly in China, producing substantial economic benefits while being accompanied by grievous pollution and food safety issues mainly due to the immoderate use of fertilizer and pesticide [1, 2]. More specifically, the amount of fertilizer application to land use in 1978 was 8.84 million tons, which has grown to 60.23 million tons in 2015, with approximately five times of net increase [3]. The pesticides and chemical fertilizers not absorbed entering the surrounding environment generate soil

Low-carbon development has recently become a growing trend in agricultural modernization. It will provide beneficial guidance for exploring driving factors of agricultural carbon emissions in Hebei Province, a typical agricultural region, and formulate relevant policy on its reduction. Calculating carbon emissions in Hebei from 1995 to 2014 demonstrated that energy and land use accounted for more than 90% of agricultural carbon emissions, and there was an increasing tendency overall with a peak value in 2010 and, to a certain extent lately, a slight decline. This paper employed transformed Kaya identity in light of local actual conditions for selecting eight influencing factors. Meanwhile, the extended STIRPAT model and ridge regression were used to make regression analysis. Results showed that contributing factors were efficiency, agricultural import, urbanization, agricultural mechanization, and population, whose 1% increase caused 0.1852%, 0.1663%, 0.1597%, 0.1573%, and 0.1329% increases in carbon emissions, respectively, while 1% growth in industry structure and agricultural affluence were responsible for 0.1475%, and changing the elastic coefficient of (0.1314-0.2958lnA)% decrease in carbon emissions, respectively, where A represented agricultural output value per capita. Furthermore, there existed an inverted U-shaped EKC between economic progress and carbon emissions. Given the above conclusions, policy recommendations were provided for effectively achieving agricultural carbon emissions reductions.

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China faces significant challenges related to global warming caused by CO2 emissions, and the power industry is a large CO2 emitter. The decomposition and accurate forecasting of CO2 emissions in China’s power sector are thus crucial for low-carbon outcomes. This paper selects seven socio-economic and technological drivers related to the power sector, and decomposes CO2 emissions based on two models: the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model and the partial least square (PLS) model. Distinguished from previous research, our study first compares the effects of eliminating the multicollinearity of the PLS model with stepwise regression and ridge regression, finding that PLS is superior. Further, the decomposition results show the factors’ absolute elasticity coefficients are population (2.58) > line loss rate (1.112) > GDP per capita (0.669) > generation structure (0.522) > the urbanization level (0.512) > electricity intensity (0.310) > industrial structure (0.060). Meanwhile, a novel hybrid PLS-Grey-Markov model is proposed, and is verified to have better precision for the CO2 emissions of the power sector compared to the selected models, such as ridge regression-Grey-Markov, PLS-Grey-Markov, PLS-Grey and PLS-BP (Back propagation neutral network model). The forecast results suggest that CO2 emissions of the power sector will increase to 5102.9 Mt by 2025. Consequently, policy recommendations are proposed to achieve low-carbon development in aspects of population, technology, and economy.