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According to United Nations reports, the worldwide population is expected to reach around 9.6 billion by 2050. This forecast emphasizes the critical role of energy and raw materials that are needed to meet the tremendous demand for goods. Consequently, firms feel pressured to establish sustainable and resilient strategic plans to acquire new process technologies and expand their capacity on time. These decisions are made at the highest management level, supported by a set of capacity expansion portfolios, to improve their competitiveness, especially in capital-intensive industries such as chemical processing. This paper investigates the sustainable and resilient capacity expansion problem for such industries within a long-term horizon. The main objective is to develop a holistic capacity expansion planning framework that fits the chemical processing industries and can be used to generate resilient scenarios while enhancing their sustainability measures. To this end, a bi-objective mixed-integer programming model was developed to solve the sustainability–resilience–profitability dilemma. The results showed a controversial relationship between the profitability of capacity expansion investments and a company’s commitment to sustainability and resilience. Furthermore, capacity expansion decisions were shown to primarily depend on the importance assigned to maximize profit as a managerial choice. However, there is no clear trend in sustainability preferences based on the sustainability weighting choice.

We consider an investment project that produces a single commodity. The project's operation yields payoff at a rate that depends on the project's installed capacity level and on an underlying economic indicator such as the output commodity's price or demand, which we model by an ergodic, one-dimensional Itô diffusion. The project's capacity level can be increased dynamically over time. The objective is to determine a capacity expansion strategy that maximizes the ergodic or long-term average payoff resulting from the project's management. We prove that it is optimal to increase the project's capacity level to a certain value and then take no further actions. The optimal capacity level depends on both the long-term average and the volatility of the underlying diffusion.

The Magdalena watershed in Colombia is the most densely populated and economically important region in the country. While Colombia is generally classified as a water-rich country, it is expected that water shortages will occur in the future without adequate planning and investments in water management infrastructures. Currently, even though all instruments required for an integrated water resource management are present in Colombia, they are employed independently from each other and thus not very efficient. To estimate the potential benefits of a more coordinated water management planning, especially in consideration of projected changes in water availability and demand in the near future, we developed a constrained welfare maximization model of the watershed (CAMARI). We ran the model with three different scenarios of future water availability, based on RCPs 2.6, 4.5 and 6.0, and with two planning modes: coordinated and uncoordinated. The results show that a coordinated planning of investments in water management infrastructures increases welfare by 2–18% over the next century in the Magdalena river basin, which corresponds to average annual savings from US$ 610 million to US$ 6.4 billion. Benefits increase as water availability decreases. Our results also show that water demand from the agricultural sector is projected to rise in future, which further underlines the necessity for robust governance mechanisms to keep conflicts between sectors to a minimum.