Unraveling interactions and priorities under sustainable development goals in less-developed mountainous areas: case study on the National Innovation Demonstration Zone for the 2030 Agenda for Sustainable Development, China.

Understanding the intricate relationships between progress and the United Nations' 17 Sustainable Development Goals (SDGs) is vital for informed and adaptable sustainable development policy formulation. This study focused on the Lincang National Innovation Demonstration Zone for the 2030 Agenda for Sustainable Development (LC-NIDZASD) in China. By evaluating sustainability scores at the county level from 2011 to 2020, the trade-offs and synergies among SDGs were explored. Priority SDGs for development were identified, and targeted recommendations were established based on these findings. The key findings are as follows: (1) The SDG index scores of Lincang and its counties showed an increase from 2011 to 2020, with scores riding from 42.1 to 52.2. SDG6 (Clean Water and Sanitation) and SDG12 (Responsible Production and Consumption) had the highest scores, while SDG1 (No Poverty) and SDG4 (Quality Education) increased significantly. However, the COVID-19 pandemic led to a decrease in the scores of SDG1, SDG8 (Decent Jobs and Economic Growth), and SDG17 (Partnerships for the Goals) in 2020 decreased compared to 2019. Decreased scores in SDG13 (Climate Action) and SDG15 (Life on Land) may be attributable to climate change. (2) The relationship between "Objectives" and ''Governance" appears to be synergistic, while ''Essential Needs" mainly shows a trade-off relationship with ''Objectives" and ''Governance." (3) To promote achievements in the construction of LC-NIDZASD, priority should be given to SDG3 (Health and Well-Being), SDG8, SDG9 (Industry, Innovation, and Infrastructure), and SDG12; SDG4 should not be ignored. (4) Overall, Lincang has made significant progress in sustainable development. However, to further consolidate these achievements, adjustments should be made for SDG7 (Energy Consumption and Production Structure). Efforts should be made to strengthen climate governance measures and improve warning and forecasting capabilities to promote the synergistic development of SDG7 (Affordable and Clean Energy) and SDG13 with other SDGs. This study's dynamic monitoring of changes in the SDGs in Lincang provides valuable insights into the synergies and trade-offs among these goals. Appropriate prioritization across various SDGs can allow for timely adjustments in sustainable management policies, ultimately contributing to the successful operation of the LC-NIDZASD.

Spatial-temporal dynamics of land use carbon emissions and drivers in 20 urban agglomerations in China from 1990 to 2019.

Urban agglomerations (UAs) are the largest carbon emitters; thus, the emissions must be controlled to achieve carbon peak and carbon neutrality. We use long time series land-use and energy consumption data to estimate the carbon emissions in UAs. The standard deviational ellipse (SDE) and spatial autocorrelation analysis are used to reveal the spatiotemporal evolution of carbon emissions, and the geodetector, geographically and temporally weighted regression (GTWR), and boosted regression trees (BRTs) are used to analyze the driving factors. The results show the following: (1) Construction land and forest land are the main carbon sources and sinks, accounting for 93% and 94% of the total carbon sources and sinks, respectively. (2) The total carbon emissions of different UAs differ substantially, showing a spatial pattern of high emissions in the east and north and low emissions in the west and south. The carbon emissions of all UAs increase over time, with faster growth in UAs with lower carbon emissions. (3) The center of gravity of carbon emissions shifts to the south (except for North China, where it shifts to the west), and carbon emissions in UAs show a positive spatial correlation, with a predominantly high-high and low-low spatial aggregation pattern. (4) Population, GDP, and the annual number of cabs are the main factors influencing carbon emissions in most UAs, whereas other factors show significant differences. Most exhibit an increasing trend over time in their impact on carbon emissions. In general, China still faces substantial challenges in achieving the dual carbon goal. The carbon control measures of different UAs should be targeted in terms of energy utilization, green and low-carbon production, and consumption modes to achieve the low-carbon and green development goals of the United Nations' sustainable cities and beautiful China's urban construction as soon as possible.