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Green initiatives are popular mechanisms globally to enhance environmental and human wellbeing. However, multiple green initiatives, when overlapping geographically and targeting the same participants, may interact with each other, giving rise to what is termed “spillover effects”, where one initiative and its outcomes influence another. This study examines the spillover effects among four major concurrent initiatives in the United States (U.S.) and China using a comprehensive dataset. In the U.S., we analysed county-level data in 2018 for the Conservation Reserve Program (CRP) and the Environmental Quality Incentives Program (EQIP), both operational for over 25 years. In China, data from Fanjingshan and Tianma National Nature Reserves (2014–2015) were used to evaluate the Grain-to-Green Program (GTGP) and the Forest Ecological Benefit Compensation (FEBC) program. The dataset comprises 3106 records for the U.S. and 711 plots for China, including several socio-economic variables. The results of multivariate linear regression indicate that there exist significant spillover effects between CRP & EQIP and GTGP & FEBC, with one initiative potentially enhancing or offsetting another’s impacts by 22% to 100%. This dataset provides valuable insights for researchers and policymakers to optimize the effectiveness and resilience of concurrent green initiatives. 

A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of complex adaptive systems—CAS science—has a significant potential to handle such challenges. We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science, the generic features of CAS, and the key advances and challenges in modeling CAS. Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms. 

Concurrently implemented green initiatives to combat global environmental crises may be curtailed or even sacrificed given the ongoing global economic contraction. We collected empirical data and information about green initiatives from 15 sites or countries worldwide. We systematically explored how specific policy, intended behaviors, and gains of given green initiative may interact with those of other green initiatives concurrently implemented in the same geographic area or involving the same recipients. Surprisingly, we found that spillover effects were very divergent: one initiative could reduce the gain of another by 22 % ~ 100 %, representing alarming losses, while in other instances, substantial co-benefits could arise as one initiative can increase the gain of another by 9 % ~ 310 %. Leveraging these effects will help countries keep green initiatives with significant co-benefits but stop initiatives with substantial spillover losses in the face of widespread budget cuts, better meeting the United Nations’ sustainable development goals. 

Extracting roads in aerial images has numerous applications in artificial intelligence and multimedia computing, including traffic pattern analysis and parking space planning. Learning deep neural networks, though very successful, demands vast amounts of high-quality annotations, of which acquisition is time-consuming and expensive. In this work, we propose a semi-supervised approach for image-based road extraction where only a small set of labeled images are available for training to address this challenge. We design a pixel-wise contrastive loss to self-supervise the network training to utilize the large corpus of unlabeled images. The key idea is to identify pairs of overlapping image regions (positive) or non-overlapping image regions (negative) and encourage the network to make similar outputs for positive pairs or dissimilar outputs for negative pairs. We also develop a negative sampling strategy to filter false negative samples during the process. An iterative procedure is introduced to apply the network over raw images to generate pseudo-labels, filter and select high-quality labels with the proposed contrastive loss, and re-train the network with the enlarged training dataset. We repeat these iterative steps until convergence. We validate the effectiveness of the proposed methods by performing extensive experiments on the public SpaceNet3 and DeepGlobe Road datasets. Results show that our proposed method achieves state-of-the-art results on public image segmentation benchmarks and significantly outperforms other semi-supervised methods. 

A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of Agent-based Complex Systems—ACS science—has a significant potential to handle such challenges. The advantages of ACS science for sustainability are addressed by way of identifying the key elements and challenges in sustainability science, the generic features of ACS, and the key advances and challenges in modeling ACS. Artificial intelligence and data science promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms. 

Summary China’s Belt and Road Initiative (BRI), designed to build critical infrastructure and coordinate economic growth, is the most significant development initiative in modern history. The BRI has a documented vision for sustainability, including environmental impact assessments and responsibility tenets. Despite this, a growing body of literature has found adverse effects of BRI projects on protected land and species. To understand corporate responsibility and regulations for companies participating in the BRI, we gathered information on 260 BRI companies using the Refinitiv Eikon BRI Connect database and the China Global Investment Tracker. The results revealed a significant gap in corporate responsibility reporting for biodiversity impacts, environmental restoration, environmental project financing and the United Nations’ Sustainable Development Goals (SDG) 14 ‘Life below Water’ and 15 ‘Life on Land’. The modest fraction of companies that we found to report biodiversity accountability highlights the need to restructure and incentivize the reporting of environmental and biodiversity risks. The current evidence of limited adherence to responsibility measures highlights a clear opportunity to align BRI development with the BRI’s vision for sustainability, and to strengthen links for policy engagement within Chinese regulatory frameworks and international obligations at the United Nations within its SDG framework.