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Accounting for Sustainability—Could Cost Accounting Be the Right Tool?

• First Online: 01 April 2020

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• Franco Ernesto Rubino 7 &
• Stefania Veltri 7  

Part of the book series: CSR, Sustainability, Ethics & Governance ((CSEG))

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The last few decades have witnessed the increasing pressures for organizations to behave in a socially and environmentally responsible fashion, and businesses have started to acknowledge the importance of sustainability, embracing the sustainability rhetoric in their external reporting and in their mission statement. One area that has not yet been investigated in depth is related to the capability of existing corporate accounting systems to measure sustainability. Development of such instrumental sustainability accounting systems will require the accounting profession to step outside its comfort zone and measure and manage external environmental and social impacts. Extending the boundary of analysis beyond the “entity” has implications for both accounting and management control system design. The chapter intends to intervene in the lively debate in literature about the usefulness and capability of corporate accounting systems to address sustainability optimally by taking into consideration positive and negative positions as regards the sustainability of accounting. It also aims to imagine if and how an accounting for sustainability might emerge and what possibilities could arise for accounting in light of a sustainability science approach.

While the chapter is the result of a joint effort of the authors, the individual contributions are as follows: Franco Rubino wrote Sects. 3 , 4 and 6 ; Stefania Veltri wrote Sects. 1 , 2 and 5 .

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Rubino, F.E., Veltri, S. (2020). Accounting for Sustainability—Could Cost Accounting Be the Right Tool?. In: Del Baldo, M., Dillard, J., Baldarelli, MG., Ciambotti, M. (eds) Accounting, Accountability and Society. CSR, Sustainability, Ethics & Governance. Springer, Cham. https://doi.org/10.1007/978-3-030-41142-8_5

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• Published: 16 September 2021

Implementation of strategic cost management in manufacturing companies: overcoming costs stickiness and increasing corporate sustainability

• Mohammad Mahdi Rounaghi   ORCID: orcid.org/0000-0002-9640-678X 1 ,
• Hajer Jarrar 2 &
• Leo-Paul Dana 3  

Future Business Journal volume  7 , Article number:  31 ( 2021 ) Cite this article

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In today's competitive world, three factors: price, quality and time have critical roles in the success of the companies to achieve success in the competition. For this purpose, the companies have to also adapt themselves to changes in technology and environment. Strategic cost management is the best way to improve the sustainable management models in the manufacturing companies. Strategic cost management has solved many of the problems and shortcomings of traditional accounting system and by accurate determination of costs, their proper allocation to products and elimination of waste, tries to create value for shareholders by using continuous improvement. The objective of this paper was to develop a management model called strategic cost management that reduced costs stickiness and increased corporate sustainability. Using strategic cost management approach can create competitive advantage for the companies, because it provides accurate cost price information so that the users can easily understand the information. The aim of the paper by introducing strategic cost management was to contribute toward accurate pricing, which could result in the increased profitability and competitiveness of the manufacturing companies in a highly competitive global market and at a market‐based price. Also, due to the growing competition among companies in providing high quality products with reasonable prices, a precise system of measurement of the cost of the product is necessary.

Introduction

In recent years, economic analysis in the planning process and in the monitoring process of the production process shows that three factors: price, quality and time have critical roles in the success of the companies to achieve success in the competition. The world faces the problem of integration between sustained business functions. The sustainability data are not sufficiently integrated. To solve this problem, organizations need information systems to facilitate their sustainability initiatives [ 1 , 2 ]. Also, businesses and academics worldwide agree regarding the benefits of sustainable development (SD). Improving reputation and branding and increasing revenues by reducing costs are the primary strategic objectives of any entity [ 3 , 4 ]. In this paper, we introduce the strategic cost management approach that helps manufacturing companies for overcoming the costs stickiness and monitoring the life cycle of products and it introduces integrated sustainable development system for manufacturing companies.

Strategic cost management is a process connecting financial management, cost management and strategic management. It involves cost optimization and financial resources preparation which are needed to achieve desired strategic market position in cost effective manner. The importance of managing costs and aligning them with the business strategy of an entity is critical especially in the midst of challenging economic times faced by businesses today. Traditionally companies have been under pressure to cut cost in the short-term without really thinking about sustainable change, impact on the people and integration with the overall business strategy. In the current business environment of increased global competition, new markets, increasing regulation and changing demographics, successful companies are changing their approach to cost structuring and control.

Over the last decade, research in management accounting has challenged the fundamental assumption that cost behavior is symmetric for activity increases and decreases. Cost behavior is an important issue in cost accounting and management accounting, as it widely affects decision-making processes. Moreover, several techniques generally used by managerial accountants and financial analysts depend mainly on cost behavior, such as conventional ABC, cost estimation and cost-volume-profit analysis. Quality management (QM) has been widely viewed as a management paradigm that enables firms to gain a competitive. Therefore, overcoming on cost stickiness is a critical issue for mangers of manufacturing companies. Also, understanding cost behavior is an essential element of cost and management accounting [ 5 – 8 ].

Cost stickiness, also referred to as asymmetric cost behavior, is a well-documented result of managerial discretion underlying the development of corporate cost compared to changes in firm activity. Managers’ decisions to maintain the resource allocations due to product market competition can be costly, especially during periods of sales decreases. Under the traditional model of cost behavior, costs are assumed to be either fixed or move proportionately and symmetrically with sales changes. The traditional model of cost behavior distinguishes between fixed and variable costs and posits a proportional relation between variable costs and underlying activity levels. Understanding sticky cost behavior is important and has direct benefits for the economy as it provides useful information to managers making decisions on cost control and to external stakeholders (e.g., financial analysts) assessing firm performance. As the global economy integrates and competes, strengthening cost management and operational efficiency becomes increasingly important to firms’ survival and development [ 9 – 14 ].

Cost management is an important part of business management in the manufacturing industry. The degree of cost management implementation is a comprehensive index to measure the level of enterprise management. In particular, firms with limited access to capital have higher costs of securing external financing during the capacity expansion periods, which increases the upward adjustment costs. When activity decreases, firms with limited access to capital may suffer more decrease in the present value of revenue generated by a marginal capacity, as these firms have higher opportunity cost of capital and thus higher discount rates compared to firms with better access to capital. Therefore, we hypothesize that limited access to capital not only reduces contemporary capacity expansions associated with sales increases, but also weakens the degree of cost stickiness when sales decrease [ 15 , 16 ].

On the other hand, cost management is an important part of business management in the manufacturing industry. The degree of cost management implementation is a comprehensive index to measure the level of enterprise management. From investors’ perspective, investors depend on the published financial statements prepared by the management that are based on available information regarding the determinants of cost behavior. From financial analysts’ perspective, predicting cost behavior is an essential part of earnings prediction [ 16 – 18 ].

In many production firms, it is common practice to financially reward managers for firm performance improvement. For decades, firms have devoted to improving the speed and efficiency of material and information flows in the supply chain, acknowledging the importance of time-based competitive advantage in the dynamic business environment. As one of the key factors in decision-making process, the evolution of product price passes critical information. Managing costs by utilizing resources effectively is regarded as fundamental to success in today's competitive environment. Cost behavior as “sticky” if costs increase more for activity increases than they decrease for an equivalent activity decrease. Sticky behavior is the result of decisions made by managers when activity decreases. When activity drops, the manager must decide whether to (a) maintain committed resources and bear the cost of unutilized capacity at least in the short-term or (b) immediately reduce committed resources and incur potentially large retrenching costs in the current period and, if activity increases in the future, incur further costs to replace resources. Traditional accounting cost models assume that fixed costs are independent of the level of activity and variable costs change proportionately with changes in the level of activity. In the common traditional model of the behavior of costs, which is generally accepted in accounting literature, costs are usually divided into two categories of fixed and variable ones in terms of changes in activity level: fixed occupants are variable. Most management accounting texts assume that unit variable costs are linear and proportional to changes in activity and that fixed costs are fixed. The proportionality and symmetry between costs and activity implies that a 1% increase in activity results in a 1% increase in costs, and a 1% decrease in activity results in a 1% decrease in costs. Stickiness might also be conditioned by existing capacity [ 5 , 19 – 26 ].

Notions of cost behavior are a key element in management accounting [ 27 ]. There are two main views about the existence of expense stickiness: rational decision-making and motivational. The rational decision-making view treats expense stickiness as a consequence of management rationally choosing between alternatives after comprehensively weighting costs and benefits. The second view is motivation-based and relates expense stickiness to managerial incentives, suggesting that managers are not expected to behave as if they were in an ideal world. Among their dysfunctional behavior, perks and earnings management reflecting different contracting stimulations are often observed [ 28 ].

Planning and control are of the important tasks of management. Cost related information that managers need them to perform these tasks may be received from classified information reflected in the financial statements. The required information in this regard cannot be easily extracted from the financial statements [ 29 ]. A business entity expenses can show different behaviors suitable to the level of activity. In traditional cost model it is often assumed that administration, general and selling costs varies according to activity level. However, recent experimental studies have revealed evidence that shows that administration, general and selling costs behave asymmetrically [ 30 ]. An asymmetric behavior is a behavior in which cost increase more rapidly. In other words, the reduction in costs at the time of declining sales is lower than when the cost increases at the time of the same level of sales. This cost behavior is called cost stickiness. Expanding researches show that economic factors such as increase in assets and uncertainty about the future can have an impact on the asymmetric behavior of cost.

Costs stickiness

Cost behavior is defined as cost reaction in response to changes in activity level. Managers who understand how costs behave, have better circumstances for predicting spending trends in various operational positions. This position allows them to plan their activities and thus plan their operating revenues better. The traditional view related to costs indicates that changes in costs have a proper relationship with increased and decreased activity level. However, recent researches about costs behaviors indicate costs stickiness. Thus the degree of increase in costs level as a result of increase in activity level is higher than the degree of reduction in costs level as a result of decrease in activity level.

According to the idea of Anderson et al. [ 31 ], there are many reasons for costs stickiness. Some of these reasons include natural reluctance to lay off employees when downsizing, firm costs and the need for time to approve a reduction in the volume of activity and management decisions for maintaining used resources which could be the result of individual consideration and leads to imposing cost to the firm. By determining the stickiness of cost, the company owners can analyze whether managers incur costs to the firm or not [ 32 ].

Managers of manufacturing companies must consider the relationship of costs with income and the effect of income changes on the costs rate when planning and budgeting the company activities for predicting the future costs and thus offer a more comprehensive budget [ 33 ]. The ultimate goal of any business unit is maximizing profits and consequently, an increase in equity. Management of each profit-oriented enterprise tries to gain maximum benefit and efficiency from using the fewest resources and one of the simplest ways to reduce consumption of resources is cost control. But this requires complete knowledge of how costs behave and the factors influencing the behavior of the cost. One of the items that should be considered in the analysis of cost behavior is the phenomenon of cost stickiness. The public and dominant view is that with declining sales, costs should also be changed accordingly. But in fact, it does not happen [ 34 ].

Today, increasing competition in domestic and international markets has forced managers to better understand their cost structure and become aware of cost orientations means how the costs change. The meaning of cost orientation is a model according which costs react to changes in activity level [ 35 ]. Therefore, it is suggested that managers calculate their costs stickiness and consider all aspects of this important issue in their decisions. Orientation or the concept of cost stickiness gives a great help to investors and shareholders. Because in companies with strong stickiness, by reduced selling, costs will change more than the time when selling increases and this will be considered as a weakness of management by the investors and shareholders; while one of the main reasons of cost stickiness is bearing the current costs to avoid more losses in the future and or more profit in the future and it depends on management decisions [ 36 ].

Review of literature

Sustainable development refers to an economic, environmental and social development that meets the needs of the present and does not prevent future generations from fulfilling their needs. In manufacturing companies, collaboration between supply chain members is important for the sustainability and competitive advantage of a supply chain. The collaborative activities in a supply chain include various joint activities for cost reduction, research and development (R&D), product development, manufacturing, marketing, distribution, and service. The commitment of companies to corporate sustainability has been frequently discussed in theory and practice. Such a commitment to corporate sustainability demands a strategic approach to ensure that corporate sustainability is an integrated part of the business strategy and processes. Also, the effective adoption of continuously developing new technologies is a critical determinant of organizational competitiveness [ 37 – 41 ].

For the first time [ 5 ] tested the hypothesis that costs are sticky and approved the presence of stickiness in the costs behavior. They established a model with administration, general and sales costs as a function of sales, and found that costs increase by an average of 55% in response to a 1% increase in net income, but decrease only by 35% against 1% reduced income. In other words, a 1% increase in net sales, costs increase by 55% but by 1% decrease in net sales, costs decrease only by 35%. Due to the lack of public information about costs related drivers, they used data of administration, general and sales costs and net income of sales for the analysis of cost stickiness, and stated that they can analyze the behavior of administration, general and sales costs based on sales net income because sales volume stimulates many parts of this cost. Subramaniam and Weidenmier Watson [ 25 ] tested the presence of behavior of stickiness in the cost price of goods sold, and the results showed a positive relationship. They also tested the effect of different economic conditions, such as rates of GDP and the different characteristics of companies, such as total assets and number of employees of companies on costs stickiness. Their results showed that in periods of economic growth, the severity of stickiness is more and in the periods that income decrease happened in its previous periods, severity of stickiness decreases. Also, by increasing the ratio of total assets to sales and an increase in the number of personnel of companies, severity of cost stickiness increases. Stickiness of sales and distribution and general and administration costs has been studied in another study by Anderson et al. [ 31 ]. The main hypothesis of this study is public sale and administration costs. After collecting data related to cost of general sales and administration and sales revenue costs of 7629 American companies in a 20-year period (1979–1998), the relationship between costs and sales was examined by multi-varibale regression relationship. The results of this study did not confirm the main hypothesis of the research and announce the general sale and administration costs of companies in the statistical population of the research, sticky.

The results obtained by Weiss [ 18 ] from a sample of 2520 out of 44,931 industrial companies from 1986 to 2005 show the issue that the sticky behavior of costs increased the accuracy of analysts in predicting revenue in total, considering the fact that prediction horizon and especial effects of industry have put this analysis under control. With regard to the classification of costs into sticky and non-sticky costs, the results of Weiss's research [ 18 ] show that the accuracy of analysts in forecasting revenues for firms with sticky cost behavior is on average 25 percent less than that of people who analyze for companies with non-sticky cost behavior. Obviously, the behavior of cost has a considerable influence on the accuracy of analysts' prediction.

In Kordestani and Mortazavi, research [ 30 ], the power of profit prediction was compared with other models by the model based on variability and stickiness of cost. The study showed that the accuracy of prediction of the model based on the variability of costs and stickiness of cost is significantly higher than the other models. In several domestic researches, stickiness of various costs has been studied. According to the results of Ghaemi and Nematollahi's research, the cost price of the sold goods and selling and distribution and general and administration costs are sticky. Another study from the same researcher showed that overhead costs are sticky, but the costs of raw materials, direct wages and financial costs are not sticky.

In other study, Khani and Shafiei [ 42 ] examined cost stickiness and its relationship with sales and the results of their research indicate an undeniable relationship between the amount of sales and company earnings with the level of company's costs. Although their findings indicate that costs do not increase in proportion to profit increase, but there is a significant relationship between them.

In other study, Banker et al. [ 43 ] examined the relationship between uncertainty and sticky behavior of cost. By examining administration, general and sales costs, number of employees and their working hours, they evaluated cost stickiness. The results indicate the presence of cost stickiness in the sample under investigation. Sepasi et al. [ 44 ] examined the characteristics of management behavior toward costs stickiness. Their studied a sample consisting 14,568 year-company and examined administration, general and sales costs for the years 1992–2011. The results showed behavioral changes in managers about cost stickiness so that the occurrence of cost stickiness phenomenon increases the confidence of managers.

Management of strategy and strategic cost management

Effective strategic management, plays an important role in the success of the company or organization. Increase in competition in the international arena, new technologies and changes in business processes, caused management to become more dynamic and important than before. Managers should always have a competitive attitude and for this purpose the company's competitive strategy is essential. Strategic attitude leads the manager to anticipate changes and products and their production process will be designed based on anticipated changes in demand and customer's needs. In this situation, flexibility is important.

In developed countries, most organizations use data of cost management. But the extent of their reliance on this information depends on the nature of the competitive strategy of the company. Many companies compete on the basis of the provision of goods and services at the lowest cost price. Some companies compete on the basis of being a leader in production and offering superior and differentiated products. The role of cost management is supporting corporate strategy by providing the information through which one can be successful in products development and their marketing. For achieving corporate sustainability, we suggest to use the instruments of strategic cost management in manufacturing companies . Today, managers use strategic cost management tools to accomplish strategies and achieve main success producer factors.

Instruments of strategic cost management are as below:

The most common system that used in many companies is activity-based costing system. Activity-based costing system which is specifies the resources consumed by each activity during the relevant period; and thus the cost of each activity is precisely calculated. Then the aggregated costs of any activity are assigned to the considered product or customer, depending on the product consumption or the customer use of that activity [ 45 ]. The other instrument is bench-marking. Bench-marking is a process that the companies try to choose the best practice as of the right activity in comparison with the leading companies, then given the success-builder factors, the company processes are improved to the level of performance of its competitors or even reach to a better level. For identification of internal and external failure factors in the companies, we suggest to use total quality management technique. Total quality management a new concept that emphasizes on precise measurement of the costs and identification of internal and external failure factors, through which a way to lower production (lean production) by continuous improvement in company processes is created [ 46 ].

For finding the precise systems of measurement of the cost, in-time production system and kaizen costing are useful tools for manufacturing companies. In-time production system is a system based on the volume of demand. In this system, a piece of product will be purchased or produced only when a sign of its consumer is received. This prevents the accumulation of inventory in workstations. Among the main objectives of this system we can mention improvement of quality and increase in productivity with an emphasis on the kaizen concept. Kaizen costing is a managerial technique through which managers and employees of the company become committed to perform continuous improvement program in the quality and other key factors of success. In the path of continuous improvement, the processes are re-engineered and non-value activities in the manufacturing process are removed or left behind [ 47 ].

The other instruments are target costing and value engineering. In target costing method, the costs are determined according to the product price. It means that first the companies determine the product selling prices, by analyzes of the market and then according to their expected profit, determine the cost price of the product. In other words, goal-oriented costing system is profit planning and cost management system that in that base it was the price, and the essential emphasis is on customers. Goal-oriented costing system focuses on the design stage and requires the participation of all specialized units [ 48 ]. Value engineering is suggested with the aim of examination of all activities of a project, from the formation of the first thought to the design and implementation and then setting up and utilization, is known as one of the most efficient and the most important economic methods in the field of engineering activities [ 49 ]. The purpose of value engineering is eliminating or modifying any factor that leads to the imposition of unnecessary costs, without hurting the core and essential functions of the system. Value engineering is the continuous improvement of design and implementation and it is not merely a program to reduce costs, but is a way to maximize the value of designs [ 50 ].

Implementation stages of strategic cost management

Implementation stages of strategic cost management include value chain analysis, strategic situation analysis and analysis of structural and administrative costs drivers.

Analysis of the value chain

Value chain analysis is an instrument for strategic analysis that helps companies to better understand the competitive advantage. Value chain analysis focuses on the whole value chain of the product from design to production and after-sales service. The basic concept of analysis is that by a thorough examination of each of the activities in the value chain, one can reveal the activities that the companies have the highest or lowest success in them from competition perspective, and plan accordingly.

Analysis of strategic situation

At this stage, the company determines its potential and current competitive advantage by examining valued activities and cost drivers which have been specified in the previous stage. Companies which have competitive strategy of cost leadership are strongly trying to reduce their costs to the level of cost of cost leadership. Cost leadership focuses on cost reduction only as far as it makes sure that it is the leader in price and the holder of the lowest cost in the market. Reduction of costs is usually done by increasing productivity in the production process, distribution or general and administrative expenses. In this management strategy, maintaining stability is a priority and the company is not looking for innovation and risk-taking, but is looking for offering products and services at competitive prices. In contrast, competitive strategy of differentiation, allows the companies to raise the price of products higher than that of their competitors and without significant reduction in costs, have high profitability. These companies, by creating differentiation between the products and creating new features, make customers willing to pay a reasonable price as a result of this differentiation. Using the product differentiation strategy, one can reduce the intensity of competition and no threat of product substitution happens for the manufacturer, because all customers become loyal to the brand of the product [ 51 , 52 , 53 ].

Analysis of drivers of structural and executory cost

Strategic Analysis of cost drivers helps companies in improvement of their competitive situation. Drivers of structural and executory cost are used to facilitate operational and strategic decision-making.

Driver of structural cost, has strategic nature because it includes programs and decisions which have long-term effects. In this regard, the following items are necessary to be noted:

Scale: For example, a retail company shall determine the number of new stores it opens during the year in order to achieve the strategic goals and competitive success.

Technology: New technologies can significantly reduce the company costs. For example, some manufacturing companies in developed countries use computer technology to show number of products that their customers use (especially large retailers), so that whenever the customers run out of the inventory in the warehouse, they send for them quickly.

Complexity of products: companies that produce a high variety of products, have high cost of planning and management of production and also high distribution costs and after-sales service. Such companies usually use activity-based costing to determine the degree of profitability of their products.

Administrative cost drivers, are the factors that companies can manage them in the short term through operational decisions to reduce costs. These factors include:

Work commitment: work commitment causes reduction in costs. The companies in which there is a strong correlation between the employees, can significantly reduce their operating costs.

Design of Production process: the sequence arrangement of equipment and the frequency of processes lead to accelerating the production process in the company. Production technology innovations can significantly reduce costs.

Relationships with suppliers of raw materials of the company: the companies can reduce their costs significantly through agreements with suppliers of raw materials on quality, delivery time and other characteristics of their required raw materials.

Conclusions

Today, sustainability emphasizes various aspects of the organization in economic, social and environmental terms, so the importance of this issue is very important for current and future generations. Most companies have come to the conclusion that in order to improve the efficiency and effectiveness of production sustainability, they need to monitor, measure and control the characteristics of sustainable production. Therefore, measuring the sustainability of production has become an important issue in production and operations.

The purpose of this paper is to design a model for achieving a sustainable development index in order to integrate the economic, social and environmental performance data of manufacturing industries. By understanding the limitations and shortages of resources, the approach of the manufacturing companies includes the acquisition of new production mechanisms and technologies. To achieve newer and more innovative technologies tailored to their production processes in order to reduce production costs and increase their market share, these companies have conducted costly research. One way to deal with a shortage of resource for companies is reduce their costs. Companies regardless of sizes and operational scales must take economic opportunities into account in the long run, limiting opportunities, and incorporating innovative solutions, sustainable development, and positive social and environmental impact into their business activities.

Small-business owners face an ongoing challenge in trying to balance the need to serve customers and meet long-term business objectives while at the same time controlling the cost of doing business. A strategic cost management strategy in which cost decisions are made according to the value they add to both the business and the customer is often the most effective strategy a small business can adopt. Good financial decisions come from an effective cost management strategy designed to maximize value and minimize both initial and ongoing costs. Although a great many of a business’s cost-based decisions involve purchasing, pricing and inventory management, it’s also important for every small-business owner to consider costs involved inside the business.

In a competitive world, paying attention to cost management to reduce costs and increase customer satisfaction are priorities. Today, noting the proper role of the choosing quality and quantity of production factors, choosing between user processes or capital in the production process and selection of appropriate technology, in determining the cost price and producing products that meet the price reasonable in accordance with the customer' purchasing power appear more than before.

Providing the required information of cost management is possible only by establishing a modern system of management accounting including the design and use of various management accounting tools within the organization. Among these tools, there are activity-based costing, target costing, Kaizen costing, product life cycle costing. Strategic cost management is effective by accurate evaluation and identification of costs in the creation of income, profitability and value creation for companies.

By a correct understanding of their competitive situation and by using instruments of cost management, companies can reduce unnecessary costs. Also strategic cost management, by providing more accurate data for the managers, helps them in the short and long-term decision-making to achieve their strategic goals.

Given the importance of understanding the costs for those inside and outside the organization, such as managers, capital market analysts, investors and auditors recommendations for future research are presented as follows:

Examination of the effect of the changes in sales on costs stickiness.

Study of the relationship between management optimism with cost stickiness in various industries.

Examination of the relationship between the cost structure with behavior of each expense.

Availability of data and materials

This paper has no associated data.

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Rounaghi, M.M., Jarrar, H. & Dana, LP. Implementation of strategic cost management in manufacturing companies: overcoming costs stickiness and increasing corporate sustainability. Futur Bus J 7 , 31 (2021). https://doi.org/10.1186/s43093-021-00079-4

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• Strategic cost management
• Manufacturing companies
• Cost stickiness
• Corporate sustainability
• Continuous improvement

Cost Management Research

Journal of Management Accounting Research, Forthcoming

64 Pages Posted: 10 Dec 2016 Last revised: 26 Oct 2017

Rajiv D. Banker

Temple University - Department of Accounting

Dmitri Byzalov

Temple University - Fox School of Business and Management - Department of Accounting

Shunlan Fang

Kent State University

University of Virginia - McIntire School of Commerce

Date Written: October 25, 2017

The traditional view of cost behavior assumes a simple mechanistic relation between cost drivers and costs. In contrast, contemporary cost management research recognizes that costs are caused by managers’ operating decisions subject to various constraints, incentives, and psychological biases. This conceptual innovation opens up the “black box” of cost behavior and gives researchers a powerful new way to use observed cost behavior as a lens to study the determinants and the consequences of managers’ operating decisions. In 2014, Banker and Byzalov presented an overview of the economic theory of cost behavior and major estimation issues. The research literature on cost management has grown rapidly in the past few years and enhanced the understanding of how managerial decisions influence observed costs. In this study, we provide a comprehensive review of recent findings and insights, with a particular emphasis on the implications of cost management for understanding issues in cost, managerial, and financial accounting, and challenges and opportunities for future research.

Keywords: managerial decisions; resource adjustment costs; asymmetric cost behavior; cost stickiness; optimistic and pessimistic expectations

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Artificial intelligence investments reduce risks to critical mineral supply

• Joaquin Vespignani   ORCID: orcid.org/0000-0003-0265-4377 1 , 2 &
• Russell Smyth   ORCID: orcid.org/0000-0001-6593-5462 3  

Nature Communications volume  15 , Article number:  7304 ( 2024 ) Cite this article

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• Energy and society
• Energy economics

This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and non-technical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the “back-ended risk premium”. We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself by shortening the duration of mining projects and the required rate of investment by reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research.

Introduction

Unlike traditional energy sources, such as oil, coal, and gas, clean energy production and storage requires an unprecedented amount of critical minerals, such as copper, lithium, nickel, zinc, cobalt, and rare earth minerals 1 . Consequently, the shortage in the supply of critical minerals that are needed to achieve global net zero targets by 2050 has gained significant attention 1 . Recently, the International Energy Agency (IEA) estimated that the investment that will be required in critical minerals between 2022 and 2030 to realize global net zero by 2050 is ~360–450 USD billion, while the anticipated supply is between 180 and 220 USD billion, implying a shortfall of 180 to 230 USD billion 2 .

A key issue in meeting this shortfall is that there is a considerable time lag between investment and production. According to the IEA, exploring and producing some critical minerals can take 12.5 years 2 . One potential way to increase critical mineral production is via technological progress through artificial intelligence (AI), which can be used at all stages of the mining process. It has been argued that the mining industry is poised to reap the rewards of AI and data-driven approaches as it deals with a complex integrated value chain of exploration, extraction, and refining that has a history of incorporating high-technology systems in order to increase productivity 3 . Applying AI to mining databases may be useful in developing operations for decision-making support 4 . Automatization using AI could provide significant economic benefits for the mining industry through cost reduction, efficiency, and improving productivity 5 .

This paper uses insights from earth science about the financial risk of project developments for some key critical minerals to develop a theory that provides a less sanguine view of the potential for AI to address the shortfall in investment in critical minerals, at least in the short-to-medium term. Some key critical minerals have lower value to investors during the initial stages of project development due to both technical and non-technical risks 6 , 7 , 8 . This type of risk is called back-ended risk and contrasts with front-ended risk, which is present in more traditional mining projects in which investors place a high value on the project in the very early stages, reducing the cost of capital. Taking this observation as a starting point, in this work we develop an economic theory of what we call the back-ended risk premium. This theory states that for minerals that exhibit back-ended risk, the required rate of return by investors is higher than for front-ended risk mineral projects, leading to a back-ended risk premium. The required rate of return is defined as the minimum amount that an investor seeks when they embark on an investment or project 9 .

We show that the increase in the cost of capital due to this risk premium leads to a theoretical slowdown in technological progress. In the infancy of the AI revolution in the mining sector, the back-ended risk premium has the potential to reduce some of the expected gains from AI-inspired technologies. The extent to which this occurs will depend on whether, and to what extent, progress in AI can reduce the back-ended risk premium by shortening the duration of mining projects and lowering the risk faced by investors.

Results and discussion

Conceptualization of the back-ended risk premium.

Many of the minerals considered critical for energy transition exhibit back-ended risk in project development caused by technical and non-technical risks occurring in the later stages of the project (six to eight years after the exploration starts) 6 , 7 , 8 . Examples of critical minerals that exhibit back-ended risk are bauxite, cobalt, lithium, graphite, niobium, nickel laterite, tungsten, scandium, rare earth, vanadium, and zinc. The existence of such back-ended risk means that investment is insufficient for these minerals to increase production in response to an increase in demand. This contrasts with the risk faced by other minerals—so-called front-ended risk minerals—such as gold, copper, and iron ore, for which the value of the project increases rapidly during the exploration stage. For these minerals, most of the investment needed for production occurs in the early stages of the project in response to an increase in demand.

In Fig.  1 , we illustrate back-ended and front-ended risk project development and show how this relates to our proposed concept of the back-ended risk premium. The vertical axis represents the valuation (or share price) that the market places on the mineral project. The horizontal axis displays the different stages of development of the mining project (exploration, scoping, feasibility, development, and mining). At time \({T}_{1}\) the valuation (or share price) of front-ended risk minerals is \({V}_{2},\) which is much higher than the valuation of back-ended risk minerals ( \({V}_{1}\) ). Figure  2 shows that the valuations of back-ended and front-ended risk projects eventually converge after the development stage. The back-ended risk premium can be seen as the relative additional rate of return required by investors for back-ended minerals projects, relative to front-ended minerals projects, and in Fig.  1 is denoted by the gray-shaded area.

This figure illustrates the relationship between the value that investors attribute to the project at various stages of the development of the mining project. The red line represents this relationship for front-end minerals, while the blue line represents the corresponding relationship for back-end minerals. \({T}_{1}\) and \({T}_{2}\) denote the beginning and conclusion of the project development. \({V}_{1},\,{V}_{2}\) and \({V}_{3}\) are three alternative valuations attributed by investors to the project.

This figure illustrates the differences in the required rate of return to investors between front-ended and back-ended minerals. We define these differences as the back-ended risk premium. \({R}_{b}\) is the required rate of return for back-ended critical minerals and \({R}_{f}\) is the required rate of return for front-ended minerals. \({T}_{1}\) and \({T}_{2}\) denote the beginning and end of the project development.

Figure  1 suggests that for back-ended critical minerals, the value to shareholders only increases in the latter stages of the project development, meaning that investment (and production) will be lower than the market equilibrium requires. Figure  2 illustrates the difference in risk premium between front-ended and back-ended risk projects or the additional risk that investors would incur investing in back-ended minerals compared to front-ended minerals. The vertical axis represents the required rate of return by investors, and the horizontal axis denotes the time between \({T}_{1}\) and \({T}_{2}\) from Fig.  1 . From this figure, we can infer the following:

Investors require a relatively larger return to be compensated by the back-ended risk premium.

The back-ended risk premium decreases as a function of time.

The back-ended risk premium is equal to the risk of front-ended minerals after \({T}_{2}\) .

Risk premiums for critical minerals

Figure  3 presents the non-technical risk premium for each of the critical minerals for 2022. The measurement of the non-technical risk premium is described in the methods, Supplementary Information Section  2 , and in the Supplementary Data. Each of the critical minerals exhibits a non-technical risk premium, relative to the front-ended non-critical mineral benchmark, consisting of coal, gold, and iron ore. The non-technical risk premium varies from rare earth elements (30.6%) to nickel (2.9%), with the average being 15.7%. The heterogeneity in non-technical risk between critical minerals largely reflects the geographical distribution of specific critical minerals, with those exhibiting higher non-technical risk concentrated in countries having less attractive investment environments. In Supplementary Fig.  3 , we show these results are robust to including the only major front-ended critical mineral (copper) in addition to coal, gold and iron ore in the benchmark of front-ended minerals.

This figure represents the non-technical risk premium for 16 critical minerals relative to a front-ended minerals benchmark consisting of coal, gold, and iron ore for the period 2022. Details of the construction of this measure of the non-technical risk premium, data, and calculations are available in the methods, Supplementary Information, and Supplementary Data.

Figure  4 presents the technical risk premium for 14 critical minerals for which we have data. The measurement of the technical risk premium is described in the methods and Supplementary Information Section  3 . Tin has the highest technical risk premium, while copper and antimony have a technical risk discount, relative to the non-critical mineral benchmark. The average technical risk premium across the 14 critical minerals is 18.4%.

This measure of the technical risk premium is based on six characteristics (crustal abundance, crustal concentration, ease of mining, ease of processing, criticality of use, and diversity of use). Details of the construction of this measure of the technical risk premium, data, and calculations are available in the methods, Supplementary Information, and Supplementary Data.

In Fig.  5 , we present estimates of the total risk premium for the 13 critical minerals for which we have data on technical and non-technical risk. Rare earth elements, tin, manganese, tungsten, and zinc have the highest total risk premium. The only mineral showing a total risk premium discount is copper, reflecting its lower technical score relative to the benchmark.

This figure illustrates the total risk premium, which is the sum of non-technical (Fig.  3 ) and technical (Fig.  4 ) risk premiums for 13 critical minerals for which we have data on both technical and non-technical risks.

Estimates of the back-ended risk premium

We provide conservative estimates of the cumulative back-ended risk premium depicted in Fig.  2 until 2035 under the IEA’s Sustainable Development Scenario (SDS) and Stated Policy Scenario (STEPS). STEPS is based on current policy settings, while SDS presents the best-case scenario for the clean energy transition. For each scenario, we calculate the total risk premium based on the weighted average of the market value of each critical mineral. Using the weighted average of the market value has the advantage that it considers differences in the relative importance of critical minerals to the clean energy transition. For the SDS, we calculate the total risk premium to be 32.5%, while for the STEPS the total risk premium is 31.6%.

Our estimates of the cumulative back-ended risk premium until 2035 are given in Fig.  6 . The cumulative back-ended risk premium by 2035 ranges between USD 660 billion and USD 678 billion in the STEPS and SDS scenarios, respectively. Based on estimates described in the methods section, in our main analysis, we use 13.49% as the Weighted Average Cost of Capital (WACC) for the benchmark front-ended minerals. In Supplementary Figs.  4 and 5 , as a sensitivity check on our main analysis, we alternatively assume that the WACC to calculate the back-ended risk premium is 12% or 16%. When the back-ended risk premium benchmark for front-ended minerals is estimated using a WACC of 12%, the cumulative back-ended risk premium decreases from USD 660-678 to USD 587-604, which can be considered a lower-bound estimate on the cumulative back-ended risk premium. In the upper bound case, when the back-ended risk premium benchmark for front-ended minerals is estimated based on 16%, the cumulative back-ended risk premium increases to the USD 775-804 billion range. This figure represents the additional cost of capital for back-ended projects and helps explain why investment in critical minerals is low even when long-term demand is high.

This figure presents the back-ended risk premium (BRP) for the Sustainable Development Scenario (SDS) and Stated Policies Scenario (STEPS), which are the two standard energy policy scenarios presented by the International Energy Agency.

Using AI to address the shortfall in critical minerals

A key feature of the AI revolution is that many roles will be automated, and machine learning procedures that heavily depend on capital will replace mundane labor tasks 10 . In a large survey of machine learning researchers, the consensus among respondents was that AI will outperform humans in many activities over the next decade 11 . The expected gains in productivity in critical minerals production that are hoped will meet the shortfall in supply will be in automation of the labor force and high-level machine intelligence, in which capital is the key component at all stages of the mining industry production process from exploration to extraction 5 .

The back-ended risk premium theory, presented above, implies that the cost of capital for back-ended minerals is higher than that for front-ended minerals. Our findings suggest that, on average, the WACC for front-ended minerals is 13.49%, whereas, for back-ended minerals, it is 4.26% and 4.44% higher, depending on the scenario (17.75% and 17.93%, respectively). Thus, in a capital-intensive industry in which productivity gains are expected to be made in AI developments, the increase in capital cost because of the back-ended risk premium is expected to be economically large, lowering productivity growth in the critical mineral sector.

In Fig.  7 , we employ a production possibility frontier (PPF) to show how the back-ended risk premium affects the potential to increase productivity in back-ended critical minerals via AI. The initial curve shows the combination between capital (K) and labor (L) that is required to produce the maximum amount of minerals for a firm or economy, in which the factors of production are any combination along the PPF of \({L}_{1}\) and \({K}_{1}\) . The green line represents a potential increase in productivity due to AI-technologies which requires more investment in capital (technology). Here capital increases from \({K}_{1}\) to \({K}_{2}\) and the initial PPF rotates outwards and to the right to \({L}_{1}\) and \({K}_{2}\) . The broken blue line represents the new PPF when the back-ended risk premium is considered. The blue line shows that some of the gains in production made by development in AI-technologies is lost due to the higher cost of capital, reflecting the back-ended risk premium. The loss in production is represented by the move from \({K}_{2}\) to \({K}_{3}\) .

\({K}_{1}\) represents the initial level of capital at the initial Production Possibility Frontier (PPF), \({K}_{2}\) illustrates the level of capital after an increase in productivity due to technological advances in artificial intelligence in mining and \({K}_{3}\) denotes the level of capital considering the impact of the back-ended risk premium. \({L}_{1}\) is the amount of labor required for the three PPFs presented.

The analysis in Fig.  7 may be overly pessimistic. It overlooks the potential for AI to potentially reduce the back-ended risk premium by reducing the cost of capital for critical minerals mining projects. AI could reduce the back-ended risk premium by reducing the duration of mining projects and reducing the required rate of return on investment. We next consider the different ways in which AI could reduce the duration of mining projects and the required rate of return on investment, although we caution the reader that AI also has limitations, which we outline below. The potential benefits of AI for reducing the back-ended risk premium should be seen in light of those limitations.

There are multiple ways through which AI could potentially reduce the time from exploration to extraction in mining projects. One way is through improved mineral mapping. AI techniques, such as drone-based photogrammetry and remote sensing, can be used to automate the process of mineral mapping, making it possible to predict with greater accuracy regions with higher potential for new deposits 12 , 13 , 14 , 15 . Deep learning algorithms have been shown to provide a very high-level of accuracy in image recognition, which can be used for mineral resource mapping with surface and sub-surface image data 12 .

Supply-side risk stemming from the geographical concentration of critical minerals in a few countries has led to increased focus on the role of AI in detecting unconventional deposits of critical minerals in situ geological deposits of oil, gas, or coal mineral deposits from secondary by-products of anthropogenic processes 16 , 17 , 18 , 19 . An advantage AI has in such contexts is that a large amount of data has been collected through fossil fuel exploration on such geological deposits, which is well suited to machine learning 20 .

A second way in which AI could reduce the risk associated with duration from exploration to extraction is by making it possible to more accurately calculate the duration of the extraction period of the mine 14 . One of the most important risks for all mining projects relates to the orebody itself - ie. there is significant uncertainty about mineral resources 21 , 22 . One specific contributor to uncertainty being higher for critical minerals than conventional minerals in this early stage is that there are relatively few sources of data on critical mineral reserves. A second point of difference is that critical minerals are recovered as by-products from refining other metals. This means that the metal supply responds not only to the price of the by-product metal but also to the price of the host metal, which affects reserve estimates 23 . Uncertainty about the potential to recover critical minerals from mine waste, such as tailings, is particularly acute 24 . A third important way in which critical minerals differ from fossil fuels is that the former can be recycled, although the implications of recycling on future reserves is difficult to quantify, adding to uncertainty about available reserves 23 . Mining and extraction methods are dictated by the geology of the orebody or orebodies. AI can be used to provide a more accurate depiction of the geology of the orebody and its associated uncertainties 25 . Several AI methods have been developed to predict the grade and recovery of mineral deposits, reducing the associated technical risk 26 .

A third avenue for reducing the time from exploration to extraction is through employing AI to improve mining productivity. Risks associated with drilling and blasting performance depend on proper rock fragmentation. AI can be used to predict rock fragmentation and provide real-time evaluation of drilling performance that improves efficiency 12 . Automated drilling can be fitted with sensors that target specific ores; hence, reducing exploration time 27 .

AI can also be used to reduce the required rate of return on investment in two main ways. One way is through reducing uncertainty with the risk of a blowout in the cost, which is particularly important for back-ended minerals given that the initial cost of capital is higher than for front-ended minerals. For example, AI can be used to forecast the capital cost of open-pit mining projects 28 . Equipment selection is the most important phase during mine planning. The capital investment in selecting the right equipment represents a major risk. AI algorithms can be used to reduce the risk with equipment selection 12 . Once the mine is in operation, AI can be used for predictive maintenance and management of equipment, minimizing repairs 27 .

AI could also reduce the required rate of return on back-ended projects through reducing the risk, particularly environmental risks, associated with such projects. There is evidence which shows that AI algorithms can be effective in reducing disasters and environmental hazards associated with energy mining 29 . This is particularly important in the case of some key back-ended critical minerals, such as cobalt and lithium.

According to the U.S Geological Survey, more than 50% of the global proven lithium reserves are concentrated in the lithium triangle between Chile, Bolivia, and Argentina. Lithium in this region is mined from salt deserts or so-called salars 30 . Extracting lithium from salars has generated long-term environmental damage, with locals complaining that lithium mining has increased the prevalence of droughts, threatening livestock farming and drying out vegetation 31 . About half of the proven reserves of cobalt are located in Congo Kinshasa. Weak institutions, corruption, and conflict in the region exacerbate environmental risks associated with mining Cobalt. The technical risks associated with artisanal mining practices in Congo Kinshaha have caused environmental degradation, including deforestation, soil erosion, water pollution, and biodiversity loss. Unregulated mining activities, using mercury and other chemicals, and inadequate waste management practices can negatively affect ecosystems and local communities in the long term 32 .

The potential impact of AI on the rate of return and duration of the project are illustrated in Figs.  8 and 9 , respectively. In Fig.  8 , the potential impact of progress in AI on reducing the risk/rate on the back-ended risk premium is represented by the shift from \({R}_{B}\) to \({R}_{{AI}}\) while the volume of this reduction is represented by the area between the blue and green lines. Formally:

\({R}_{B}\) represents the required rate of return for back-ended critical minerals, \({R}_{F}\) denotes the required rate of return for front-ended minerals, and \({R}_{{AI}}\) indicates the required rate of return after considering advancements in artificial intelligence technology. \({{T}}_{1}\) and \({T}_{2}\) denote the start and end times of the project developments, respectively. \({f \left(\right. R}_{b}\) ) and \({f\left(\right.R}_{{AI}}\) ) are functions of \({R}_{B}\) and, \({R}_{{AI}}\) , respectively.

\({R}_{B}\) represents the required rate of return for back-ended critical minerals, and \({R}_{F}\) denotes the required rate of return for front-ended minerals. \({T}_{1}{{{\rm{and}}}}{T}_{B}\) denote the start and end times of the project developments, respective \({ly}.\,{T}_{{AI}}\,\) indicates the required rate of return after considering advancements in artificial intelligence technology. \({f\left(\right. T}_{B}\) ) and \({f\left(\right. T}_{{AI}}\) ) are functions of \({T}_{B}\) and, \({T}_{{AI}}\) , respectively.

In Fig.  9 , the impact of AI progress on reducing the mining project time is represented by the shift from \({T}_{B}\) to \({T}_{{AI}}\) (the area between the blue and the green lines represents the volume). Formally:

In Fig.  10 , we demonstrate the effect of lowering the rate of the back-ended risk premium and a reduction in the duration of mining projects because of advancements in AI technology. For simplicity, we assume a 50% reduction in risk. Since our estimates are proportional, readers can infer proportional reductions at different percentages. A 50% reduction in risk resulting from AI improvements leads to a corresponding decrease in the back-ended risk premium, falling within the range USD 330 (STEPS) to USD 341 billion, while a 50% shortening of project duration resulting from AI improvements leads to a corresponding decrease in the back-ended risk premium, falling within the range USD 330 billion (STEPS) to USD 334 billion (SDS).

This figure describes the cumulative effect of artificial intelligence on the back-ended risk premium (BRP) for the Sustainable Development Scenario (SDS) and Stated Policy Scenario (STEPS), which are the two standard energy policy scenarios presented by the IEA. a shows the cumulative impact of a 50% reduction in the risk rate. b shows the cumulative impact of a 50% reduction in project duration.

Investment in AI can eliminate the negative impact on the back-ended risk premium on investment in and production of critical minerals, which is key to achieving net zero. Various combinations of reductions in the risk/return associated with back-ended projects, as well as the duration of mining project time, may achieve a zero back-ended risk premium. Again, conclusions about the potential for AI to address the back-ended risk premium are subject to AI’s limitations outlined below.

Subject to these limitations, the implication of this result for the energy transition is that an immediate large-scale direct investment in critical minerals’ projects by governments between USD 660 and USD 678 billion is needed over the next decade. This amount is consistent with the shortage in investment estimated by the IEA 2 . The back-ended risk premium is an additional cost to achieve net zero 2050 that policymakers, thus far, have not considered when estimating the shortfall in investment.

What if meeting carbon net zero by 2050 is not possible?

Some studies have posited that, given existing mining constraints and known reserves of critical minerals needed for clean energy transition, replacing existing fossil fuel sources for energy requirements with renewable alternatives will not be possible by 2050. For example, one study finds that of 29 necessary metals in the lifecycle of renewable energy technologies, known reserves of eight metals might be depleted by 2050 33 . Another study presents several scenarios for transition to carbon net zero, concluding that ultimately, global reserves of cobalt, nickel, and lithium may not be enough to resource the number of batteries needed to power the electric vehicles needed for clean energy transition 34 .

What does this mean for the back-ended risk premium and AI’s potential mitigation of this risk premium? Our interpretation is that the conclusion from such studies reinforces the importance of attracting investment in back-ended critical minerals to reduce the expected shortfall to realize the clean energy transition. It also highlights the importance of investment in AI applications in development and exploration to reduce the duration of mining projects and reduce the required rate of return on investment. Micheaux suggests that if existing critical mineral reserves are not sufficient to resource clean energy transition, a new social contract may be required that limits energy demand 34 . A pessimistic conclusion from this might be that if commitment to energy transition by 2050 is ignored this might erode the motivation for global support for net zero emissions from governments and lending agencies. In such circumstances, other solutions would need to be found, and financing investment in back-ended critical minerals would be less important, making the back-ended risk premium less important. However, this is unlikely. Given the global recognition of the threat posed by climate change, it is very unlikely that commitment to clean energy transition will be eroded even if meeting 2050 targets proves not to be feasible. If commitment to the clean energy transition did look like faltering, reducing expected demand for critical minerals, the added uncertainty would increase the back-ended risk premium by increasing the required rate of return on investment 35 . In this sense, the potential that political commitment to clean energy transition, broadly defined, could wane is a non-technical risk, which increases the uncertainty associated with investing in critical minerals, meaning investors require a higher rate of return. This applies a fortiori to critical minerals that have a high likelihood of running out before 2050. All things equal, the back-ended risk premium for these critical minerals will be higher because the uncertainty about their utility to the clean energy transition is particularly acute. However, it is important to note that the likelihood of a given mineral running out before 2050 is just one non-technical risk among many. Of the eight minerals that Moreau et al. 33 conclude might be depleted by 2050, we consider four in this study (cobalt, nickel, tin, and zinc). It is noteworthy that each of these four critical minerals has a relatively low non-technical risk premium (see Fig.  3 ). More generally, most likely, even if critical minerals do not provide the full solution to resourcing the clean energy transition, they will at least still provide some of the solution, in conjunction with other initiatives, and further investment in critical minerals will be needed to facilitate this.

Limitations on AI as a solution to the back-ended risk premium

We show that improvements in AI have the potential to reduce the back-ended risk premium. However, AI is going to require further investment to ensure the required gains and there is uncertainty about whether and when the benefits of AI will be realized. While it is often touted that AI will lead to improvements in productivity in mining and a reduction in mining project risks, there is a distribution of AI applications in mining and mineral processing in terms of maturity on the Gartner hype cycle for developing new technologies 36 , with many technologies still early on the curve (especially at ‘peak of inflated expectations’). AI has experienced a number of ‘hype cycles’ in which unrealistic expectations have preceded periods of under-delivery, funding cuts, and slowdowns in investment in R&D 37 .

Recent meta-studies have also revealed a worrying tendency for overly optimistic AI performance reporting, due to data leakage and lack of reproducibility 38 , 39 . Messeri and Crokett argue that “AI solutions can exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do” 40 . AI faces specific challenges in mining and mineral processing projects. One of the main challenges facing the mining sector in exploiting the potential of AI is addressing the skills gap, with many workers requiring reskilling or upskilling to take advantage of AI 4 , 27 , 41 .

A second set of challenges facing AI in mining studies is the lack of high-quality training data 14 , 37 , 42 . The application of AI has been limited by the use of small datasets 26 , 42 . Cost forecasting applications are typically based on relatively few data points 4 . Given that the exploration of critical minerals is relatively new and that there are relatively few critical minerals (and particularly back-ended critical minerals), the lack of databases and experimental data to train and test AI models is particularly acute.

A third related problem is that mining operations often take place in remote locations making access difficult. Abrasive materials, dust, and humidity that are commonplace in mines do not create a congenial environment for the deployment of digital technologies. In underground mining, installed sensors need to be resistant not only to dust and humidity but also to blasting that can damage the senses. Transmitting the data can be problematic due to the limited bandwidth of communication networks employed in underground and open-pit mining. Connectivity can be particularly weak and unstable in deep mine sites 27 . This makes the storage and transmission of useful data challenging 43 .

Policy recommendations

The main problem that back-ended critical minerals has is that they provide lower value to investors during the initial stages of project development and exploration, due to technical and non-technical risks. Our analysis suggests that the most important stages to invest in AI to reduce the back-end premium is in applications between exploration and development.

It is essential to highlight that investing in mining in these stages is distinct from investing in AI made by governments, the private sector, and global funding agencies in general. In 2021–2022, the United States Federal Government, as the global leader in AI investment, spent USD 3.3 billion on AI, while worldwide private sector investment in AI was USD 91.9 billion 44 . In comparison, mining companies were expected to spend just USD 218 million on AI globally in 2024 45 with only a fraction of this invested in AI in supporting development and exploration in back-ended critical minerals.

Our suggestions below for areas of investment in AI apply to governments, the private sector, and lending agencies such as the World Bank and IMF. The argument for government investment in AI applications is that it is broad-based, and the benefits of technical breakthroughs can extend beyond a single firm or sector. However, there is also a strong argument for the critical minerals sector to invest in AI to reduce the back-ended risk premium, given it is in their interests to attract investment. Improvements in AI in the development and exploration phase can reduce the back-ended risk premium by both reducing the duration of the project and reducing the required rate of return on investment.

Most AI applications in mining have focused on the development and exploration phase, which is where AI is particularly useful in mitigating technical risks 4 , 15 . One specific AI application in which governments and mining companies could invest is in data-driven prospectivity modeling, where random forest 46 and extreme learning machine models 47 have been used to more accurately predict recovery rates and reduce the time from exploration to extraction. A second related AI application is in mineral mapping in which drilling sensors, geophysical-geochemical-remote sensing surveys, and 3D geological modeling can be used to more accurately predict locations with the most potential for mineral deposits 48 , 49 , 50 , 51 .

A third area for potential investment would be in AI methods, such as artificial neural networks 52 and extreme learning models 53 , to predict the grade and recovery of mineral deposits. A fourth promising area for investment in AI is in the recovery of critical minerals from mining waste. AI has proved particularly useful in improving secondary recovery approaches, such as adsorption 54 , 55 .

Lithium extraction in Chile, Bolivia, and Argentina is plagued by water scarcity. Scarcity of water and water management is one of the main challenges of mineral processing plants that lead to delays 56 . Automated ‘dry’ or water-savvy sensing AI techniques can be employed to minimize these risks 25 . The benefits of using critical minerals, such as lithium, to facilitate the clean energy transition will be muted if they are extracted in an environmentally unfriendly way. More generally, investment in AI-powered environmental monitoring systems, such as smart earth technologies, could help mitigate the impact of minerals such as cobalt and lithium on the environment by detecting and addressing pollution in real time 56 . One of the biggest obstacles to the successful adoption of AI in addressing back-ended risk is the lack of data. Creating publicly available material datasets that can be used as a source of model training and testing is a common recommendation for further AI applications in mining 42 . Given the public goods nature of having open access material datasets, this is one area in which governments and international lending agencies could invest.

In this section, we describe how we constructed the non-technical and technical risk premiums and how we use this information to estimate the back-ended risk premium.

Non-technical risk premium

In Supplementary Section  2 , we construct indexes for the non-technical risk of 16 major critical minerals and a benchmark index consisting of the average for coal, iron ore, and gold, which are three representative non-critical front-ended minerals 6 . These 16 critical minerals account for more than 90% of the market value of the 50 critical minerals, as defined by the U.S. Department of Energy. There is no geological data on proven reserves for the other 34 critical minerals, although it is expected that more knowledge on global proven reserves will become available as AI applications improve, making it possible to calculate non-technical risks for other critical minerals in the future. The non-technical risk indexes are the product of the investment attractiveness index from the Annual Survey of Mining Companies 57 , weighted by country-proven reserves of each individual mineral (see Supplementary Tables  1 and 2 ) 30 , 58 .

Where \({w}_{c,{cm}}\) is the proven reserves of critical mineral m in country c as a percentage of the world’s proven reserves of minerals and \(S\) is the investment attractiveness index score for country c from the Annual Survey of Mining Companies 57 .

Using these indexes, we construct a measure of the non-technical risk premium, which is the critical mineral non-technical risk expressed as a percentage of the non-technical risk of the non-critical front-ended mineral benchmark.

Where \({IAI}\) is the investment attractiveness index and NC and C denote non-critical minerals and critical minerals respectively.

Technical risk premium

Sykes et al. 59 measured the technical risk of minerals for 49 minerals on the basis of six characteristics (crustal abundance, crustal concentration, ease of mining, ease of processing, criticality of use, and diversity of use). Details of these definitions are given in Supplementary Information Section  2 . Each of the minerals was given a score of either zero, 0.5, or 1 for each characteristic. Thus, technical risk for each mineral takes a value between zero (lowest) and six (highest), with higher values indicating lower technical risk. In Supplementary Fig.  2 , we show the scores of this measure of technical risk for 14 critical minerals and our benchmark of front-ended non-critical minerals, consisting of the average for coal, gold and iron ore. Using the technical risk index in Supplementary Information Section  3 , we construct a measure of the technical risk premium, which is expressed as the percentage difference between the critical mineral technical risk and the technical risk of the non-critical mineral benchmark.

Where TR is the technical risk of the non-critical mineral benchmark (NC) and critical minerals (C), from Sykes et al. 59 .

Back-ended risk premium

The back-ended risk premium is estimated as follows:

The total risk premium is the sum of the standardized technical and non-technical risk premium/discount with possible values in absolute values between zero and 1. WACC is the weighted average cost of capital for the average front-ended mining company. To estimate the WACC, we employ a random sample of 368 mining companies, spanning the period 2007 to 2023, giving a total of 2609 observations. The dataset consists of 368 mining companies engaged in mineral production worldwide, including but not limited to gold, copper, iron ore, and coal and is compiled from Bloomberg. In the Supplementary Data, we provide the dataset and all our calculations. For these firms, the WACC was 13.49%, which is consistent with the finding reported in Rasnosz 60 that the WACC of mining companies typically ranges from 8% to 20%, with an average of approximately 14%. In our main analysis, we use 13.49% as the WACC. In sensitivity analysis, we use a lower bound value of the WACC of 12% and an upper bound value of 16%. Our benchmark estimation from Eq.  4 for the back-ended risk premium is 4.44% for the SDS and 4.26% for the STEPS.

Data availability

The data is available in Excel format in the Source Data.  Source data are provided with this paper.

Code availability

Calculations are coded in Excel format and are available in the Source Data.

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Acknowledgements

We acknowledge the valuable comments of participants at the following conferences: Fourth Italian Workshop of Econometrics and Empirical Economics: “Climate and Energy Econometrics”, ERIA Working Group Meeting on the Supply Chain of Critical Minerals, Monash Business School, Climate Workshop: Navigating the Energy Transition, and presentations at Geoscience Australia, the Sustainable Minerals Institute of the University of Queensland and the School of Economics at Griffith University. We also acknowledge the help of our research assistants, Thanh Huong Bui and Lisa Chan.

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Vespignani, J., Smyth, R. Artificial intelligence investments reduce risks to critical mineral supply. Nat Commun 15 , 7304 (2024). https://doi.org/10.1038/s41467-024-51661-7

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