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To mitigate climate change and achieve sustainable development, Chinese governments have introduced "1+N" "dual-carbon" policy system aimed at reducing CO 2 emissions, transforming energy structures and controlling pollution. An effective policy mix and synergy is crucial for achieving the emission reduction targets and for ensuring the effectiveness of the policies, and it is therefore of great practical importance to provide a pathway for policy synergy based on assessing policy effectiveness. This paper selects three policies, namely the carbon Emission Trading System (ETS), Coal Control and Clean Utilization (CCCU), and Green Financial Reform and Innovation Pilot Zone (GFPZ), sets up ten scenarios, adopts difference-in-differences (DID) models to evaluate the impact, and give a pathway for policy synergy. The results show that ETS, CCCU, and GFPZ policies have significant positive effects on carbon emission reduction, energy structure transformation, and industrial environmental pollution control, respectively; CCCU has synergistic effects with ETS and GFPZ policies in controlling coal consumption and increasing the completion of industrial pollution control investments, respectively; while GFPZ policies have conflicting effects with CCCU and ETS in reducing carbon emissions and coal consumption, the GFPZ policy has conflicting effects with CCCU and ETS in reducing carbon emissions and controlling coal consumption. Finally, the study proposes a phased policy support system around carbon emission reduction in the power generation industry, energy structure transformation, and industrial environmental pollution control before 2030 and from 2030 to 2060. [ABSTRACT FROM AUTHOR]

Universities, the important locations for scientific research and education, have the responsibility to lead ecological civilization and low carbon transition. Ecological footprint evaluation (EFE) is usually used to measure sustainability of campuses. Although it can provide guidance and reference for overall campus planning, it lacks effective significance for individual behavior, especially when the reduction of carbon emissions is the aim. On the other hand a possible solution can be represented by machine learning. It can identify the key factors that will influence individual's overall carbon emissions caused by students' daily behavior, it can be used to find effective ways to reduce individual carbon emissions. This paper applied EFE and machine learning to comprehensively evaluate campus sustainability and students' carbon emissions. Huazhong University of Science and Technology (HUST), a "University in the Forest", was used as a study case in China. Even if HUST is endowned with a forest coverage of 72%, here we showed that its Ecological Footprint Index was −12.52, indicating strong unsustainability. This is mainly due to the high energy and food consumption, caused by the large population living in the campus and the lacking of energy saving measures. The per capita ecological footprint was relatively high, compared with other universities in the world, which meant more efforts needed to be done on ecological sustainability. Low carbon emission is a key feature for a sustainable campus. Based on the questionnaire survey delivered to 486 students who live in the campus, their daily active data were collected in terms of students' personal clothing, food, housing, consumption and transportation. And their associated carbon emissions were calculated based on emission intensities of Chinese population. Based on 486 detailed datasets, machine learning was then used to identify the key daily behavior to influence students' total carbon emission. Results showed that making behavior changes in air conditioning, food and electric bicycle were the most effective ways to reduce carbon emissions. Finally, while effective suggestions were proposed based on qualitative and quantitative evaluations, it is concluded that it is imperative for universities in China to formulate effective low-carbon policies, to achieve sustainable development and to confront global climate change. • A review of main initiatives of sustainable campus construction worldwide. • An evaluation combining campus ecological footprint and individual carbon footprint. • The ecological footprint of "China Forest University" reflects unsustainable. • Machine learning identifies internal driving force of campus carbon emissions. • Targeted guidelines on campus low-carbon construction are provided. [ABSTRACT FROM AUTHOR]