Steel Supply Chain Research Articles

Decarbonization pathways analysis and recommendations in the green steel supply chain of a typical steel end user-automotive industry

China's automotive industry, the world's largest producer and consumer, has reached a crucial stage in its transition from rapid expansion to high-quality and low-carbon development. Considering the decarbonization of the whole automotive industry, in addition to reducing the CO2 emissions from fuel supply and exhaust pipes, which account for 65–80 % of the total value chain CO2 emissions, the automotive industry should also accelerate the decarbonization of raw materials, which contribute to 18–22 % of these CO2 emissions. Many automotive manufacturers are collaborating with steel producers on green steel initiatives due to the high CO2 emissions from steel production, the integrated nature of the steel supply chain, and the need for early planning in low-carbon steel production routes transformation. To clarify the carbon emission characteristic of the automotive steel supply chain, this research establishes a mathematical method for calculating cradle-to-gate carbon footprint of automotive steel product based on BF (Blast furnace)-BOF (Basic oxygen furnace), Scrap-EAF (Electric arc furnace), and DRI (Direct reduced iron)-EAF steel production routes. The cradle-to-gate carbon footprints of BF-BOF, Scrap-EAF, and DRI-EAF steel production routes are 1711.84 kgCO2/t-sp (steel product), 916.39 kgCO2/t-sp, and 2738.53 kgCO2/t-sp. A projection of CO2 emissions in automotive steel supply chain from 2020 to 2060 has been investigated, taking into account production structure adjustment, scrap recycling, and low-carbon electricity adoption, as well as the demand for automotive steel products. Projections from 2020 to 2060 indicate that the CO2 emission intensity of the automotive steel supply chain will decrease by 28.25 % by 2030 and 93.99 % by 2060, with total emissions dropping by 28.47 % and 96.92 %, respectively. This research identifies effective decarbonization measures in the automotive steel supply chain, including internal and end-of-life (EOL) steel scrap recycling, which offer significant CO2 emissions reduction without increased costs, demonstrating high cost-effectiveness. In a word, this research provides a technical framework and data foundation for decarbonizing the automotive steel supply chain.

Data-driven robust optimization for a sustainable steel supply chain network design: Toward the circular economy

Steel is the indispensable cornerstone of modern life and its remarkable strength and adaptability render it a fundamental pillar of worldwide advancement and economic growth. However, the steel sector faces significant challenges, including the need to incorporate sustainable practices and circular economy principles, especially enhancing occupational safety, optimizing water and energy resource utilization, and effectively utilizing by-products within the steel supply chains. This paper addresses these gaps by presenting a bi-objective data-driven optimization model formulated to design a sustainable forward-reverse steel supply chain network. Sustainable development is studied by examining total cost, occupational safety, and environmental impacts of water and energy consumption and transportation system, concurrently. Incorporating circular economy principles, the collection of steel scrap under take-back policies and utilizing steel slag, a by-product of steel production, is considered. To address uncertainties, a data-driven robust optimization method is employed which harnesses historical data to construct a dynamic uncertainty set using support vector clustering. The model is applied to a real case study in the Iranian steel sector. Results demonstrate the effectiveness and robustness of the proposed method, showing that, on average, a 5 % increase in costs correlates with a 7 % improvement in environmental impacts.

How digital platforms can be leveraged to enable closed material loops – An example of the steel supply chain

Increasing demands for limited natural resources accelerate rethinking their usage and processing. A focal point of interest lies in the steel industry, given its substantial contribution to emissions and the notable attribute of steel being fully recyclable. Hence, closing material loops to ensure the preservation of material value and supply security is of substantial importance. Yet, until today, supply chains are still characterized by interrupted information streams that prevent circular material flows. Digital platforms are attributed to overcoming these shortcomings due to their ability to moderate ecosystems and render technological connectivity. However, industrial companies lack knowledge of how digital platforms can be used to design closed material loops. Therefore, this paper is built on a longitudinal case study of a joint venture between a recycling and steel melting plant company aiming at creating a digital platform to close the material flow of steel. Six design priorities, structured along the three core building blocks of digital platform ecosystems and the four core dimensions of collective action theory (CAT), have been derived to guide managers in designing digital platforms for the CE-specific context. This study presently contributes to understanding the relationship between digital platforms and CE.

Techno-economic optimisation of steel supply chains in the clean energy transition: A case study of post-war Ukraine

The steel industry's clean energy transition can enable new market creation and economic growth stimulation. Yet, the most efficient and feasible pathway to decouple the sector from fossil fuels remains unclear, particularly within developing nations and unstable socio-political contexts. Here, a blueprint for reconfiguring plant locations and reallocating resources is developed through a Ukrainian case study under two scenarios, which capture potential post-war conditions. Framed by regrowth of Ukraine's export-oriented steel industry and prospective European Union accession, green iron and steel trade strategies are devised. A steel supply chain optimisation model underpins the techno-economic, spatially granular analysis of energy and material flows, which utilises the inputs from a separate cost-minimised renewable energy, green hydrogen, and green ammonia production model. Results show that optimal supply chain configurations rely on mixed emissions-free energy profiles, the emergence of new steelmaking sites nearby high-quality renewables, regional alliances for green iron and steel market creation, and multi-billion-dollar investment. Mature nuclear and hydro power critically reduce costs in the near-term, whilst the rapid expansion of solar and wind energy infrastructure underpins production system scale-up. To simultaneously rebuild the 22 million-tonnes-a-year Ukrainian steel industry and transition to near-zero emissions by 2050, infrastructure investment surmounts to $62 billion, given full liberation of Ukrainian territory. Near-term investment is necessary to ease the pace of change, and although mobilising capital of this magnitude will be challenging, convincing carbon prices favour decarbonisation efforts.

STS – Steel Transportation Service

Currently, a prominent challenge faced by steel companies and retail shops is their dependence on intermediaries or brokers during transactions. This reliance leads to the imposition of additional fees that businesses must pay these intermediaries for facilitating deals. The proposed project aims to address these service- related challenges by establishing direct communication channels between steel companies and retail shops. This innovative approach allows them to forge direct connections without incurring any extra fees to brokers. In the conventional scenario where steel companies and shops rely on brokers for transactions, the negotiation process for steel prices and quantities often becomes time- consuming. Our project presents a solution to this existing problem by empowering steel companies and shops. Through our user-friendly website, we facilitate a streamlined and efficient process for negotiating and finalizing deals. By eliminating the need for intermediaries, businesses can enjoy a more convenient and cost-effective transaction experience, fostering direct communication and enhancing overall efficiency in the steel supply chain.

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

In response to the growing complexities in supply chain management, there is an imperative need for a data-driven methodology aimed at optimizing inventory allocation strategies. The purpose of this research is to enhance the efficiency of allocation and operational scheduling, particularly concerning the stock keeping units (SKUs). To achieve this, one year's operational data from a specific organization's SKUs is taken and machine learning tools are employed on the data collected. These tools are instrumental in identifying clusters of SKUs that exhibit similar behaviour. Consequently, this research offers recommendations for rational inventory allocation strategies that are finely attuned to the unique characteristics of each SKU cluster. Results obtained reveals substantial disparities between the recommended strategies for the organization's SKUs and those typically found in the literature such as same strategy cannot be used for all different types for products. This underscores the critical importance of adopting a tailored approach to supply chain management. Furthermore, the research demonstrates the remarkable efficiency of unsupervised machine learning algorithms in determining the optimal number of segments within the SKUs. The current research differentiates from others in a way that in most of the research, the holistic data-driven approach is underutilized, right from the selection of the clustering algorithm to the validation of segments.

Atomic Diffusivities of Yttrium, Titanium and Oxygen Calculated by Ab Initio Molecular Dynamics in Molten 316L Oxide-Dispersion-Strengthened Steel Fabricated via Additive Manufacturing.

Oxide-dispersion-strengthened (ODS) steels have long been viewed as a prime solution for harsh environments. However, conventional manufacturing of ODS steels limits the final product geometry, is difficult to scale up to large components, and is expensive due to multiple highly involved, solid-state processing steps required. Additive manufacturing (AM) can directly incorporate dispersion elements (e.g., Y, Ti and O) during component fabrication, thus bypassing the need for an ODS steel supply chain, the scale-up challenges of powder processing routes, the buoyancy challenges associated with casting ODS steels, and the joining issues for net-shape component fabrication. In the AM process, the diffusion of the dispersion elements in the molten steel plays a key role in the precipitation of the oxide particles, thereby influencing the microstructure, thermal stability and high-temperature mechanical properties of the resulting ODS steels. In this work, the atomic diffusivities of Y, Ti, and O in molten 316L stainless steel (SS) as functions of temperature are determined by ab initio molecular dynamics simulations. The latest Vienna Ab initio Simulation Package (VASP) package that incorporates an on-the-fly machine learning force field for accelerated computation is used. At a constant temperature, the time-dependent coordinates of the target atoms in the molten 316L SS were analyzed in the form of mean square displacement in order to obtain diffusivity. The values of the diffusivity at multiple temperatures are then fitted to the Arrhenius form to determine the activation energy and the pre-exponential factor. Given the challenges in experimental measurement of atomic diffusivity at such high temperatures and correspondingly the lack of experimental data, this study provides important physical parameters for future modeling of the oxide precipitation kinetics during AM process.

A country-level multi-objective optimization model for a sustainable steel supply chain

Steel supply chains have been pushed to consider environmental and social aspects, other than financial, however, in the context of Operational Research, the few papers proposing mathematical formulations and algorithms tackle only few dimensions of the problem. This study proposes a solution for sustainable steel production by formulating a multi-objective, multi-level, multi-modal, multi-product, and multi-period model and also devising an evolutionary algorithm for the problem. The results provide a Pareto front mapping the conflicting nature of economic, environmental and social objectives; show how changes in the production technology and transportation mode impact the objectives, and how locations with social vulnerability influence the decision of where and when to locate facilities. This paper provides a broad-ranging formulation and the results show its potential to help decision makers of the steel supply chain to make decisions considering not only economic factors.

The integrated planning of outgoing coil selection for retrieval, multi-crane scheduling, and location assignment to the incoming and blocking coils in steel coil warehouses

In this paper, the problem of scheduling crane-operated warehouses in a triangle stacking style is investigated. In a steel supply chain, warehouses play a significant role in establishing a connection between the producer and the customers. Therefore, improving their performance results in a competitive advantage in the supply chain. One of the challenges of steel coil warehouses is the selection of outgoing coils out of the candidate coils for retrieval. Despite its importance, this problem has not been investigated in the studies relevant to steel coil warehouses. In this study, in addition to the selection of outgoing coils, sequencing of the operations on the incoming, outgoing, and blocking coils, location assignment to the incoming and blocking coils, and crane scheduling are considered in an integrated manner. First, a mixed integer linear programming model is proposed. Further, a Greedy heuristic algorithm and two metaheuristics are developed to solve large-sized instances of the problem. Metaheuristics include a Greedy Randomized Adaptive Search Procedure (GRASP) and a hybrid algorithm that combines Ant Colony System with the GRASP (ACS-GRASP). Finally, performances of the proposed algorithms are compared over randomly generated instances as well as instances which are generated based on the real data obtained from Isfahan’s Mobarakeh Steel Company. To conduct this analysis, various performance evaluation metrics are applied. Computational results indicate that ACS-GRASP converges slower than GRASP, but is more reliable and efficient. On the other hand, in large-sized instances, the Greedy algorithm runs extremely fast and can find solutions which are comparable to the metaheuristic solutions.

Integrated knowledge management in the supply chain: Assessment of knowledge adoption solutions through a comprehensive CoCoSo method under uncertainty

The natural dispersion of supply chain (SC) elements and the distribution of knowledge within these elements have necessitated an integrated knowledge management (KM) process in the SC. The use of KM poses many challenges for the members of the SC, and the key to the survival of the SC lies in identifying and solving these challenges. For this reason, this paper aims to appraise the challenges of knowledge adoption in the steel SC and proposes solutions to address these challenges. To determine the significance of challenges in the SC and rank the effective knowledge adoption solutions to address these challenges, a new method called Comprehensive CoCoSo (CoCoCoSo) is introduced based on the CoCoSo method. This method consists of three parts: weighting the challenges, weighting the experts, and ranking the solutions, respectively. In the first part, a new process for weighting the challenges based on CoCoSo is presented; in the second part, the CoCoSo method is enhanced by the ideal average concept for weighting the experts, and finally, a new prescription of CoCoSo is introduced in the ranking part of the knowledge adoption solutions. Also, type-2 fuzzy sets (T2FSs) are used to consider the uncertainty in experts' opinions. T2FSs are much stronger compared to classical fuzzy sets because they have fuzzy membership degrees and prepare more degrees of freedom to consider uncertainty. Then, the challenges in the steel SC are determined using experts' opinions, and then the importance of these challenges is assessed. Furthermore, solutions to face these challenges are outlined, and then using the CoCoCoSo method, the efficiency of solutions to solve the challenges is evaluated and ranked according to their efficiency. Among the proposed solutions, affirmative leadership for KM adoption in SC and creating reliable teamwork have been identified as the most effective solutions. Also, sensitivity analysis shows that the proposed method is sensitive to the weight of the challenges. Finally, the validity of the proposed method has been confirmed by comparing it with well-known methods in the literature. Additionally, using the different degree index, the superiority of the proposed method over well-known methods in the literature has been determined.

How will CBAM affect the decarbonisation of steel industry in China? A system dynamics approach

EU's Carbon Border Adjustment Mechanism (CBAM) has been promulgated. The steel industry is included in CBAM, which will undoubtedly affect the export trade of China's steel manufacturers to the EU. It is necessary to analyse the impact of CBAM on China’s steel industry quantitatively. By establishing a system dynamics model that can simulate the operation mechanism of the steel supply chain in China, we have clarified the impacts of CBAM on the China's steel export and carbon reduction. Moreover, we have reasonably set up scenarios that represent the possible decarbonisation paths of China's steel industry and evaluated the implementation effect of each policy in these scenarios. The analysis shows that CBAM will lead to a decrease of 32% in export volume and a loss of 58% in export profits for China's steel exporters. The environmental benefits of CBAM are limited. Due to the low trade exposure, CBAM is unable to promote a large-scale carbon reduction in the China’s steel industry. Although CBAM will bring great pressure to steel exporters, a comprehensive policy of adopting more emission reduction measures can greatly improve the carbon reduction efficiency, effectively reduce the negative impact of CBAM, and maintain the stability of steel exports.

Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation

The cost-reducing potential of intelligent supply chains (ISCs) has been recognized by companies and researchers. This paper investigates a two-echelon steel supply chain scheduling problem that considers the parallel-batching processing and deterioration effect in the production stage and sufficient vehicles in the port delivery stage. To solve this problem, we first analyze several sufficient and necessary conditions of the optimal scheme. We then propose a heuristic algorithm based on a dynamic programming algorithm to obtain the optimal solution for a special case where the assignment of all ingots to the soaking pits is known. Based on the results of this special case, we develop a modified biased random-key genetic algorithm (BRKGA), which incorporates genetic operations based on the flower pollination algorithm (FPA) to obtain joint production and distribution schedules for the general problem. Finally, we conduct a series of computational experiments, the results of which indicate that BRKGA-FPA has certain advantages in solving quality and convergence speed issues.

Motivational Factors Affecting Knowledge Sharing in Steel Industry Supply Chain: A Mixed Qualitative-Quantitative Method Analysis

The knowledge sharing creates a collaborative environment which contributes to creation of flexibility and meeting demands in supply chain. It guarantees the access of members to external knowledge and overall supply chain improvement in competitive environment. The steel industry as the second “key” and “strategic” industry after oil and petrochemical industry in Iran as one of the important countries in middle-east and due to its importance for development but also due to the characteristics of its cost accounting system is selected as case study. Regarding to, this study aimed to design a comprehensive list containing all knowledge sharing (KS) motivational factors for steel industries supply chain. Thus, a mixed method of qualitative (a three-phase Delphi method) and quantitative (decision-making trial and evaluation laboratory-DEMATEL) has been adopted. At first stage, the 33 motivational factors of KS were identified through literature review. Then using Delphi method, respondents were provided with the findings from the literature to shortlist and merge the factors based on steel industry supply chain features. Due to different theoretical and practical standpoints of views about KS motivators in steel industry supply chain, 30 respondents (experts) participated. The final factors extracted via Delphi method was 15. Then, DEMATEL method was used to find interdependence among factors. At the end, cost accounting system for each factor were asked from knowledge management departments in steel supply chain. The results indicated that majority of the costs in steel industry supply chain is spent in decreasing power and sense of ownership of knowledge, improving culture and expectations, and improving personal contact and interaction, team culture, rewards, strategic thinking, and individual management of time, respectively. Whereas, the results of DEMATEL technique indicated that team culture, power, and sense of ownership of knowledge; personal contact and interaction; trust; culture; and expectations are the 5th high ranking factors. Although there are plenty of techniques used for improving team culture such as community of practice, after action review, Knowledge caf’e, it is not so dominant in cost accounting system. Hopefully, departments consider power and sense of ownership of knowledge more carefully. Managers in steel supply chain pay more attention to cultural issues by having educational courses. Strategic thinking is not among the highest-ranking factors, but there are costs spent for it in steel supply chain which should be pondered upon it.

Barriers Prioritization of the Indian Steel Industry Supply Chain: Applying AHP and Fuzzy AHP Method

This study seeks to identify possible key barriers in the supply chain of the Indian steel industry. Each of these hindrances has a significance in serving the store network and refining the economy of India. The study is a mix of theoretical and useful structures, which would zero in on those critical barriers in the steel supply chain. It includes a theoretical examination of the barriers in the Indian steel industry and ranking of these barriers using multicriteria methods, that is, analytical hierarchical process (AHP) and fuzzy analytical hierarchical process (FAHP) approaches. The main finding is the identification of key barriers in the Indian steel industry supply chain and prioritizing them according to the severity of their impact. Eleven potential key barriers have been considered in the study for analysis. There can be more barriers in the Indian steel industry. This study exposes the application of both methods, that is, AHP and FAHP, for ranking identified barriers.

Analysis and evaluation of challenges in the integration of Industry 4.0 and sustainable steel reverse logistics network

Industry 4.0 (I4.0) is a comparatively new phenomenon, and it is most probable that developing countries would face challenges in adapting it for improving the processes of supply chains and moving toward sustainability. The steel industry is the core of industrial growth, and it has an indispensable role in the development of countries. Steel is a highly recyclable product, meaning that it can be reused infinitely, increasing the significance of its reverse logistics. Although many studies have been conducted in the area of I4.0 and supply chain management, less attention has been devoted to finding and analyzing potential challenges of I4.0 technologies integration in steel reverse logistics activities. Therefore, this study is conducted to identify and analyse the challenges to efficient integration of I4.0 and sustainable steel reverse logistics system. Data collection is conducted with the assistance of qualified experts familiar with the steel supply chain and I4.0 concept. The interrelations of challenges are specified by Interpretive Structural Modeling, and the final ranking of challenges is determined through the Fuzzy Analytical Network Process. After validating the completed questionnaires, the absence of experts in I4.0, lack of clear comprehension of I4.0 concepts, training programs, and governmental policies and support are determined as the most critical challenges. Finally, the results and discussion, which can help practitioners in the efficient adoption of I4.0 to have a sustainable reverse logistics system, are presented.

Economic feasibility analysis of carbon capture technology in steelworks based on system dynamics

In the context of the carbon neutral goal proposed by China, the Iron and Steel Industry (ISI) which is responsible for considerable carbon emissions has to take effective actions to emissions reduction. As Carbon Capture, Utilization and Storage (CCUS) has been recognized as an important technical way to reduce industrial carbon emissions in recent years, its application in the ISI is still greatly limited due to the inherent large cost and high economic uncertainty. Besides, there is also a lack of relevant research on the application of CCUS in the ISI. Therefore, it's necessary to study whether steelworks will participate in the CCUS system by applying carbon capture technology. Focusing on steelworks and employing the System Dynamics method, this paper analyzes technical and economic feasibility of applying carbon capture from the perspective of iron and steel supply chain, and identifies three key variables of the system through sensitivity analysis. The results demonstrate that steelworks can successfully apply carbon capture technology under specific conditions. Three key factors include improving the converting efficiency of reinvestment into capture technology, establishing an appropriate scheme of government subsidies, and fostering environmental awareness of downstream consumers to increase their willingness to pay for low-carbon steel.

Analyzing environmental sustainability enablers for an Indian steel manufacturing supply chain

PurposeThe purpose of this paper is to establish the structural relationships between the environmental sustainability enablers (ESEs) for the steel supply chain in India and also to classify them on the basis of driving and dependence power with an objective of achieving enhanced environmental sustainability performance.Design/methodology/approachThe ESEs were identified through extensive literature review and discussion held with experts from the Indian steel industry. The interpretative structural modeling (ISM) approach was applied to an Indian steel supply chain to arrange these 12 ESEs in different hierarchies of the structural framework on the basis of their driver dependence power and also diagraph was developed to classify them.FindingsThe water pollution control system, air pollution control system and soil pollution control system were found to be in the bottom hierarchy of the ISM framework and these enablers were also observed in the driving quadrant of the diagraph. These driving enablers should be given priority for its full-scale development, management and monitoring to sustain and enhance the environmental sustainability in the Indian steel supply chain, whereas top management commitment, environment compliance certification and government regulation and incentives are the antecedents for carrying out the sustainability program.Research limitations/implicationsThe outcomes of the current study are limited to the Indian steel manufacturing company in specific but the results will not change significantly for the steel sectors in India. However, an empirical analysis can be used to explore and validate the structural framework for its acceptability.Practical implicationsThe outcomes from the methodology provide the basis for an Indian steel manufacturing industry to develop the right strategy in their quest for environmental sustainability.Originality/valueAlthough the study on ESEs of various industries was reported in the literature, the comprehensive study to identify the significant ESEs related to the Indian steel supply chain for environmental sustainability in specific have been hardly carried out. The current study will definitely be a valuable addition to environmental sustainability literature in general and steel supply chain environmental sustainability in specific.

Reaching sustainability through collection center selection considering risk: using the integration of Fuzzy ANP-TOPSIS and FMEA

Companies collect their used products and use them again to enhance their environmental and social profiles besides obtaining profit from using them again in the forward flow. By adding collecting and handling products at the end of their lifecycle in the context of reverse logistics, companies create closed-loop supply chains. The processes of a network can be sustainably optimized by adjusting the performance of its different facilities regarding sustainability dimensions. In circumstances that there is more than one facility on a specific level, they can be prioritized by different means such as multi-criteria decision-making or risk assessment techniques. In evaluating facilities with each method, some criteria might not be addressed appropriately, so combining these methods can produce a satisfactory answer. In this study, the performance level of each collection center regarding the sustainability of their performance and the risk associated with their operations in a reverse steel supply chain is evaluated. For this aim, the Failure Mode and Effects Analysis, Fuzzy Analytic Network Process, and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution have been integrated. The main contribution of this study is addressing the performance level of collection centers, as a vital facility in reverse logistics, from both operational and risk aspects considering sustainability dimensions. The applicability of the suggested methodology is demonstrated through its application in a steel manufacturer in the north of Iran, and the required sensitivity analyses are presented. Finally, managerial insights are discussed to enhance the performance of collection centers.

Optimal decisions in supply chains with a call option contract under the carbon emissions tax regulation

Carbon emissions tax has been recognized as an effective market-based mechanism to curb carbon emissions, which makes every participant in the supply chain rethink their optimal operational decisions. Motivated by an iron and steel supply chain under the carbon emissions tax regulation, this paper investigates the optimal operational decisions for both supplier and retailer in the supply chain with a call option contract. Specifically, this paper considers a supply chain consisting of one risk-neutral supplier and one risk-averse retailer. To effectively match supply with the uncertain demand while reducing the carbon emissions, the retailer can choose to purchase call options from the supplier, and thus is allowed to adjust the final order quantities after the demand realization. The optimal order and production policies with and without the call option contract are derived. Further, it is demonstrated that the call option contract can benefit both the retailer and supplier, improve the performance of the whole supply chain and decrease invalid carbon emissions. In addition, the introduction of call option contracts can mitigate the effect of carbon emissions tax and risk aversion on the order quantity of the retailer. Finally, numerical experiments based on a real-world iron and steel supply chain are conducted to validate the theoretical findings and provide useful managerial insights.

System dynamics simulation for CO2 emission mitigation in green electric-coal supply chain

CO2 emissions from electric-coal combustion remain dominant in aggregate CO2 emissions of China. Few of existing studies explore the systemic interactions among the whole life cycle of green electric-coal supply chain. This study proposes a CO2 emission estimation model from the perspective of direct costing, and then develops a system dynamics (SD) model to simulate the scenarios on CO2 emission mitigation in the whole life cycle of green electric-coal supply chain. System dynamics model contains four sub-systems, including production subsystem, transportation subsystem, storage subsystem and consumption subsystem. Major sources and key impact factors of CO2 emissions changes as well as mitigation scenarios for electric-coal supply chain greening are discussed. Results show that 1) the developed SD model in electric-coal supply chain is tested to be robust; 2) the potential for CO2 emission mitigation in transportation system is larger than the other systems; 3) the effective scenario for emission mitigation is to increase the proportion of railway transportation during transportation, expand gas extraction during production and increase technological investment of gas extraction during transportation. The framework of scenario simulation using developed SD model could be a reference to the greening of other energy-intensive supply chains, such as iron and steel supply chain etc.