Prospects for global sustainable development through integrating the environmental impacts of economic activities

Abstract

Human economic activities drive the production and consumption of goods and services, contribute to the achievement of the United Nations Sustainable Development Goals (SDGs). However, the extent of economic growth's influence on the SDGs remains unclear. To fill this knowledge gap, here, we quantified the environmental effects of economic activities and explored correlations between environmental effect and achieving SDGs. We developed six Environmental Footprint Indices, with a higher score indicating better efficiency or lower burden. Here we show that the various Environmental Footprint Indices had synergistic and trade-off effects on most SDG targets indices, but the synergistic effects prevailed. As income increased, the correlation between Environmental Footprint Indices and SDG target indices gradually strengthened. improved production efficiency and consumption changes notably advance SDGs, especially in low-income group countries. Our work provides scientific insights into the impact and prospects of environmental regulation required for achieving the SDGs by 2030.

Fig. 1. The environmental footprint of economic activities.

a The environmental footprint of economic activities, including six environmental footprints, WF water footprints, EF energy footprints, CF carbon footprints, MF material footprints, NF nitrogen footprints, and LF land footprints, displays the global average levels of unit USD and per capita environmental footprint (refer to the “Method” section). b Comparison of the environmental footprint by per USD and per capita among countries; the coordinate value is the percentage difference from the global average, where symbols represent countries with four income categories. The size of the circles represents the total population of the countries. The country names corresponding to the ISO-3 codes in the figure are provided in Table S1 in the supplementary information. c environmental footprints trends in four income categories, income H high-income, income UM upper-middle-income, income LM low-middle-income, income L low-income. d Fitting the relationship between the FI footprint intensity score, FP footprint pressure score, and the SDG index separately.

Fig. 2. Positive and negative effects between targets and environmental footprints (EFPs) Index across four income categories.

Multiple regression approach was adopted to explore the positive or negative effects of the environmental footprint on targets (using the Target Index as the dependent variable and the Environmental Footprint Index as the explanatory variable, with all data ranging from 0 to 100). In all models, statistically significant factors (p < 0.05) were selected. The coefficient of the independent variable serves as a positive or negative effect. In the graphs (a–d) respectively depict the positive or negative effects between the environmental footprint (WF water footprint, EF energy footprint, CF carbon footprint, MF material footprint, NF nitrogen footprint, LF land footprint) and targets for four income categories. On the left side of the graphs, blue lines represent positive effects, while red lines represent negative effects, the sum of the coefficients of each environmental footprint index is displayed on the left, representing the positive or negative effects of each footprint index on all targets. On the right side, the corresponding targets are displayed, with the thickness of the lines indicating the coefficients of the as an explanatory variable for each target. The length of the environmental footprint represents the sum of their coefficients for the target, indicating the positive and negative strength. For the specific meaning of “target”, it is provided in the PDF which is available on the website as follow. (https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202022%20refinement_Eng.pdf) or the Table S8 in the Supplementary Information.

Fig. 3. Identification of importance of sectors impacting the environmental footprint index.

The horizontal variables represent sectors differentiated in the calculation of environmental footprint index, and the vertical variables represent the different types of footprint intensity (FI) and footprint pressure (FP) in each sector, for example, CFI represent carbon footprint intensity, CFP represent carbon footprint pressure. A greater importance (darker color blocks) index indicates greater significance of the FP/FI of the sector on the environmental footprint index. Methods for measuring feature importance in machine learning are applied, where the Accuracy Decrease method, within Random Forest, assesses feature importance by removing each feature individually and measuring the resultant decrease in model accuracy, indicating how critical the feature is to the model’s performance.

Fig. 4. Synergistic effects of SDG targets under the 2030 scenario simulation.

a–d Shows the changes in the average environmental footprint index of four income categories in the different scenarios and (e–h) shows the changes of SDG targets in 2030 compared to those in the business-as-usual (BAU) scenario. For the specific meaning of “target”, refer to the PDF at (https://unstats.un.org/sdgs/indicators/Global%20Indicator%20Framework%20after%202022%20refinement_Eng.pdf).

Fig. 5. Research method framework.

The research framework is divided into four parts, with the analysis of the environmental footprint accounting and the assessment of the SDGs target index as the data foundation of this study. The Computable General Equilibrium (CGE) model and regression analysis are used to predict future environmental footprints and the SDGs target index.