The Optimal Technological Development Path to Reduce Pollution and Restructure Iron and Steel Industry for Sustainable Transition

China is the world’s largest iron and steel producer and Jing-Jin-Ji (Beijing-Tianjin-Hebei) region accounts for nearly 1/3 of the national iron and steel production, while it is facing serious air pollution. Among the top 10 worst polluted cities in China, seven were located in Hebei province in 2014. Recent years Jing-Jin-Ji region has been promoted iron & steel industry with green clean technology for accelerating sustainable economic transition. This paper tries to response the basic questions: How can we reduce pollution and restructure the iron and steel industry for sustainable economic transition in Jing-Jin-Ji? How can the iron-steel industry achieve its 13th five year plan targets? How does its outlook look like in the next 10 years? For the analysis, we develop a dynamic optimization model to explore the optimal technological development path of iron and steel industry under the environment (CO2, SO2, NOx, and PM2.5) in combing with overcapacity reduction targets over the next 10 years. The results show that increasing capacity of scrap-EAF and DRI-EAF technologies can significantly co-decrease CO2, SO2, NOx and PM2.5 by 50%, 60%, 57%, and 62% respectively. The optimal technological portfolio indicates that the production share of EAF technology will increase with the potential increase trends of scrap volumes. The paper indicates that in China, iron and steel production shift from BOF to EAF technology is an optimal way for lower energy/CO2 and air pollutants emissions, and for iron and steel industry transition to green and sustainable development. The paper argues that reducing iron and steel production volume does not mean stopping iron and steel industry development, but low-carbon and green development in the iron & steel industry, it can achieve the goal for sustainable transition in the region

Development of Robust Land-use Decisions in Eastern Europe under Technology, Climate, and System Change: The Case of Ukraine

The states of Eastern Europe (Ukraine and all the adjacent European states Belarus, Hungary, Moldova, Poland, Romania, Slovakia) have experienced technology and system change in land use since the 1990s. Their total land area exceeds the land area of Mexico or Indonesia, their total gross domestic product (in current US dollars) is between those products of Mexico and Indonesia, and their total population is higher than population of the Russian Federation or Japan. Some Eastern European states are in the world top-five producers of corn, rye, oat, triticale, buckwheat, potato, carrot, turnip, apple, gooseberry, raspberry, blueberry, plum, currant, milk (sheep), honey, flax, and other agricultural goods. It is found the agricultural production value of Romania, Slovakia, and Ukraine has higher efficiency (in the terms of water and energy use) than that of Belarus, Moldova, Poland, and Hungary. Therefore, it was expected the regions of Ukraine bordering with Romania and Slovakia are of the highest agricultural productivity. This hypothesis is confirmed by the data of capital and labor use as well as the data of agricultural production value. At the lack of energy resources, in Ukraine water appears to be a critical agricultural production factor. Moreover, the regions of Ukraine experiencing a water deficit happened to be the most vulnerable ones substantiating the well-known hypothesis on growing role of water resources for sustainable development. Because a water demand depends on the weather conditions and climate changes, the robust land-use decisions are to be developed in order to contribute to the world food security. For instance, Ukraine is transforming from a global breadbasket to a global foodbasket attracting significant investments to food production and export. The strategic investments and operational land-use decisions are based on such modern systemic risk measures as (conditional) value-at-risk, robust variant of mean or maximum loss

Advanced Stochastic Optimization Modeling of the Water-energy-food Nexus for Robust Energy and Agricultural Development: Coal Mining Industry in Shanxi province, China

In this presentation, we discuss a modeling framework able to carry out an integrated systems analysis of interdependent energy-food-water-environmental systems while accounting for the competition to those systems posed by restricted natural resources under inherent uncertainties and systemic risks. The case study focuses on developments of coal industry in water-scarce regions of China. Coal is the main energy source in China responsible for country’s energy security. However, coal-based industries consume large quantities of water, which exacerbates the problem of water scarcity. The model accounts for water consumption by various coal mining, processing, and conversion technologies, as well as water and land requirements by different crops and management systems. Uncertain water supply and demand require robust solutions that would ensure demand-production balances and other (environmental, social) constraints in all scenarios. The model derives robust interdependent strategic and adaptive decisions using the “public-private partnership” principle. Strategic long-term decisions comprise the choice of coal-related technologies, land allocation, crop portfolio, and management technologies, while adaptive decisions concern trade and water management. Systemic risks and energy-food-water security considerations are characterized by quantile-based indicators arising due to systemic interdependencies among the systems and decisions of various stakeholders and potential adversaries. Robust solutions provide insights into how to develop and coordinate, in a sustainable way, the complex linkages and trade-offs, at spatial and temporal scales, between energy, agriculture, and water sectors, as well as how to manage potential systemic risks inherent to them. The model explores new coherent energy-food-water-environmental policies accounting for local-global interdependencies induced by national-international trade, as well as self-sufficient local solutions

Robust Rescaling Methods for Integrated Water, Food, and Energy Security Management under Systemic Risks and Uncertainty

The aim of this presentation is to discuss robust, non-Bayesian, probabilistic, cross-entropy-based disaggregation (downscaling) techniques. Systems analysis of global change (including climate) processes requires new approaches to integrating and rescaling of models, data, and decision-making procedures between various scales. For example, in the analysis of water security issues, the hydrological models require inputs that are much finer than the resolution of, say, the economic or climatic models generating those inputs. In relation to food security, aggregate national or regional land-use projections derived with global economic land-use planning models give no insights into potentially critical heterogeneities of local trends. Many practical studies analyzing regional developments use cross-entropy minimization as an underlying principle for estimation of local processes. However, the traditional cross-entropy approach relies on a single prior distribution. In reality, we can identify a set of feasible priors. This is relevant, in particular, for land-cover data. Existing global land cover maps (GLC2000, MODIS2000, GLOBCOVER2000) differ in terms of spatially resolved estimates of land use, (e.g., crop, forest, and grass lands). We present novel general approach to achieving downscaling results that are robust with respect to a set of potential prior distributions reflecting non-Bayesian uncertainties, that is, data that are incomplete or not directly observable. The robust downscaling problem is formulated as a probabilistic inverse problem (from aggregate to local data) generally in the form of a non-convex, cross-entropy minimization model. The approach will be illustrated by sequential downscaling aggregate model projections of land-use changes using the Global Biosphere Management Model, with case studies from Africa, Brazil, China, and Ukraine. The approach is being used to harmonize alternative land-cover maps and to develop hybrid maps

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes

Integrated Management of Land-use Systems under Systemic Risks and Food-(bio)energy-water-environmental Security Targets: A Stochastic Global Biosphere Management Model

Interdependencies among land-use systems resemble a complex network connected through demand–supply relations, and disruption of the network may catalyze systemic risks affecting food, energy, water, and environmental security (FEWES) worldwide. This paper describes the conceptual development, expansion, and practical application of a stochastic version of the Global Biosphere Management Model (GLOBIOM), a model that is used to assess competition for land use between agriculture, bioenergy, and forestry at regional and global scales. In the stochastic version of the model, systemic risks of various kinds are explicitly covered and can be analyzed and mitigated in all their interactions. While traditional deterministic scenario analysis produces sets of often contradictory outcomes, stochastic GLOBIOM explicitly derives robust decisions that leave the systems better off, independently of what scenario occurs. Stochastic GLOBIOM is formulated as a stochastic optimization model that is central for evaluating portfolios of robust interdependent decisions: ex ante strategic decisions (production allocation, storage capacities) and ex post adaptive (demand, trading, storage control) decisions. For example, the model is applied to the case of increased storage facilities, which can be viewed as catastrophe pools to buffer production shortfalls and fulfill regional and global FEWES requirements when extreme events occur. Expected shortfalls and storage capacities have a close relation with Value-at-Risk and Conditional Value-at-Risk risk measures. The Value of Stochastic Solutions is calculated to present the benefits of the stochastic over the deterministic model