Corporate Venture Capital (CVC) has been receiving increasingly more attention all over the world as a special way for accessing new ideas and innovative opportunities through minor-share investing in established companies. The purpose of CVC investments may either be purely financial or to pursue strategic goals. Organisations often seek to take actions that impact positively on sustainability by assembling related knowledge and technologies. These resources may come from invested startups through the use of a CVC programme. This research aims to measure and analyse the Corporate Venture Capital programmes of companies listed in the ISE B3 Corporate Sustainability Index. To this end, a three-step methodology was conducted. First, a systematic review of the literature took place, followed by a review of companies based on secondary sources such as their websites. Finally, a survey was developed and was opted to survey companies through their Investor Relationship (IR) public channels. Results show that 27 of the companies listed in the ISE B3 Index have CVC programmes that contemplate organizational initiatives besides investment. In this sense, one can claim that at least 70% of the ISE B3 Index companies are somehow involved in CVC operations. The results contribute to the literature on corporate venture capital and sustainability by showing that companies spend from 10% to 15% of their capital in sustainable businesses in order to remain competitive.

The safe and sustainable management of healthcare waste (HCW) is essential for minimizing environmental impacts and protecting public health, particularly in developing countries with limited logistical infrastructure. Despite the growing adoption of routing optimization in HCW logistics, few studies integrate waste generator segmentation with algorithmic planning. This study proposes an optimization approach based on the Periodic Vehicle Routing Problem (PVRP), incorporating a segmentation of waste generators by volume. Two solution methods, the Clarke and Wright (CW) heuristic and Particle Swarm Optimization (PSO), are applied and compared through a real-world case study in Paraná, Brazil. Results show that PSO significantly outperforms CW in reducing travel distance and CO₂ emissions. For small generators, PSO achieves reductions of up to 41% in distance and 41.37% in emissions, compared to CW’s 35.42%. For large generators, PSO was reduced by 22% and 21.81%, respectively. The proposed method demonstrates the potential for scalable, data-efficient waste management strategies. This research contributes to sustainable urban logistics by bridging segmentation and routing optimization in resource-constrained settings, offering actionable insights for policymakers and planners.

The agricultural sector faces an escalating strategic challenge: reconciling the imperative to increase food production with the need to mitigate environmental impacts amid accelerating climate change. Although Digital Twin (DT) technology is a promising tool for agricultural management, existing models remain anchored in operational optimization, treating sustainability merely as a constrained parameter rather than a core strategic principle. This paper proposes the Sustainable Agriculture Digital Twin Framework (SADTF), a conceptual architecture advancing three original contributions to the smart agriculture literature. First, it models the sustainability-productivity dynamic as a formally defined strategic paradox, operationalizing this inherent tension as a bi-criteria optimization problem with a Pareto-optimal solution space rather than a conventional single-objective trade-off. Second, it introduces a dedicated Sustainability Layer designed exclusively to generate trade-off matrices across competing objectives aligned with the United Nations Sustainable Development Goals, marking a fundamental architectural departure from prior agricultural DT models. Third, it advocates a paradigm shift in the DT's role, moving it from an operational instrument to a strategic governance tool. It embeds Life Cycle Management (LCM) as the central mechanism, translating simulation outputs into strategic directives within a continuous PDCA improvement cycle. Developed through a systematic literature review and formally specified using a mathematical data flow pipeline, the framework is demonstrated concretely through a hypothetical soybean production scenario. Ultimately, the SADTF establishes a structured foundation for next-generation agricultural systems, moving beyond short-term optimization to govern the strategic transition toward practices that reconcile high productivity with long-term environmental sustainability.