Energy-Based Economic Sustainability Protocols

Abstract

In this paper, a sustainability framework for global and scalable payment systems is introduced. It is based on energy and resource consumption and pollutant classes and is inspired by ISO14040 principles. This paper aims to provide guidance for the implementation of blockchain-based technologies in a Life-Cycle Assessment methodology. The impact criteria adopted in this first approximation are at the stakeholders’ level. Enhancement through Enterprise Resource Planning software integration is considered to extend the impact allocation to the level of products and services. The system is designed on environmental economic models based on resources. A continuous depletion in the quality of exchangeable output is also modelled with respect to raw material consumption. We also consider the geophysical coordinates of pollutant emissions and the concurrent emission of pollutants affecting the quality of such outputs. This framework aims to be initially applied to the CO_2eq indicator, which is identified by a set of aerial pollutants with global warming potential as proposed by the Intergovernmental Panel on Climate Change. Nonetheless, an incentive scheme within the so-defined payment system is possible and herein suggested, including the extension to other impact criteria (e.g., pollutants released in water and soil). Multiple approximations are made in order to overcome the difficulty in sampling reservoirs of natural resources, such as (1) disregarding regeneration rate and physical limits of raw material reservoirs and (2) estimating the minimum amount of pollutants affecting the perceived quality of economic transactions. Eventually, sampling policies are outlined as fundamental tactics to foster the effectiveness of this framework.

1. Introduction

The history of economics only started to account for sustainability issues at the beginning of last century. The physiocratic school was one of the early attempts to incorporate sustainability into economics by considering land as a primary resource for wealth generation [1]. Energy economics progressively emerged as a new approach that aimed to integrate sustainability accounting into economics. The energy economics approach differs from previous methodologies that dealt with externalities. Originally, it introduced the concept of energy cost as part of the production cost and provided a novel interpretation of value and its impact on society based on physical principles [2]: two examples are Pigou’s analytical solution, which aims to internalize external costs, and the Coasian approach, which sets an upperbound to social costs and allows transferring them in a secondary market [3,4]. In the last decades of the past century, environmental awareness further emphasized the need for sustainability accounting, and energy economics offered a way to recontextualize existing approaches: energy economics has the potential to posit a new basis of valuation that incorporates the principles of physics into economic relationships [5]. The goal of this paper is to emphasize the importance of transparent accounting systems with bottom-up decision-making processes and to propose a methodology that integrates Life-Cycle Assessment (LCA) into blockchain-based protocols for sustainable economies. The purpose of this work is to improve interoperability of LCA frameworks by contributing to overcome the limitation of utilizing proprietary data (e.g., Ecoinvent or SimaPro) [6], of lack of specificity of monetary data (e.g., Environmental Input–Output in Exiobase (EIO) (Environmental Input–Output in Exiobase—https://www.exiobase.eu/, accessed on 23 May 2023)), and of standardization of indicators and metrics [7,8].

2. Background

This work is motivated by the main limitations affecting: (i) the regulation of the commons, (ii) data accessibility and homogeneity in LCA accounting, and (iii) risks of misuse of environmental reporting (e.g., mismatches between the value of environmental resources that are physically bounded and their pricing).

2.1. Limitations on Impact Assessments

The concept of the commons refers to public resources that are freely accessible, but their regulation is often lobbied for business opportunities rather than preservation of ecological equilibrium (e.g., mitigating the discharge of a business’s external costs). In this paper, the term “commons” refers to energetic resources such as raw materials, air, water, and biological ecosystems, disregarding if property or usage rights exist in public or privatized form.

Life-Cycle Assessment (LCA) is a framework for assessing the potential environmental impacts of products; its adoption is challenged due to problems with consistency of data and consensus and exchange of life-cycle data. Examples of factors of data unavailability are legally binding (e.g., property or usage rights) non-homogeneous metrics and allocation methods and different standards in procurement activities [9]. Economic input/output models offer a less data-intensive approach than LCA methods [10] but lack specificity in accounting (e.g., allocating payment flows to specific commodity or company features [11]).

One challenge in embedding sustainability criteria in financial reporting for policymaking and risk assessment is the limited comprehensiveness of indicators in international frameworks. As an example, indicators solely relying on CO_2eq may promote the adoption of technologies based on Rare Earth Elements (REE) without considering the effects of mining on common resources. Another concern is the lack of disclosure regarding the origin of procurement, which prevents open validation of actual allocations (e.g., utility companies claiming to produce renewable energy may use technologies that require non-renewable resources [12]); a deficiency of this kind is found in the Land Use, Land-Use Change, and Forestry (LULUCF) sector and in European regulations applied to the Emission Trading Scheme (ETS), which stated that biomass had an emission factor of 0 and its CO₂ emissions could be withdrawn from total emissions (Part 5, section 2 in No. 601/2012/EU; point 15 in Decision No. 525/2013/EU and No. 529/2013/EU). This kind of uncertainty increases the risks of double expending and misuse of impact assessments in favour of stakeholders benefiting from competitive advantages in negotiation power or exercising exploitation rights. The economic nature of resources, characterized by different degrees of excludability and rivalry, exacerbates this problem. For example, air is considered fully non-excludable, while water is not always fully non-excludable. Additionally, water consumption and pollution are more sensitive to location due to its different physical state and control volume compared to air [13,14]. Therefore, water holds a higher degree of rivalry compared to air considering impact criteria of climate change [15].

2.2. Opportunities for Blockchain-Based Impact Assessment

Hybrid approaches embedding LCA and input/output models) in conjunction with blockchain-based payment tracking systems may improve the effectiveness and uptake of environmental assessment practices. Examples of potential applicability are traceability and accessibility of the historical transaction records of supply chains from origin to products and even by-products; persistence of and public access to data; and flexibility to elicit, design, and deploy consensus-based indicators that are universal for industrial domains and yet specific for each subdomain (e.g., metrics of global warming risks associated with by-product pollutants). Another benefit of blockchain-based payment-tracking systems is persistent data availability as an asset for computational sustainability research, such as in the field of reservoir computing [16].

On the whole, we consider that a strength of a blockchain-based protocol for impact assessment would be the capability to increase the resolution of environmental accounting measures and flexibly incorporate new metrics so as to address the need for more comprehensive indicators across industries [17]. We also argue that such indicators should be based on physically bounded resource availability (e.g., total available energy), so as to inform the economic activities that follow from use of resources, and from policies shaped on digital frameworks that could inter-operate through interconnected industries.

3. Materials and Methods

3.1. Circular Economy Reformulation: About Energy-Based Protocols on Resources and Impacts

In this section, we will critically discuss a reformulation of the circular economy concept that is particularly suitable for the sustainability framework described in this paper. While the circularity paradigm promotes the reuse of raw and recycled materials, resulting in a reduction in the depletion of raw materials, we encounter two limitations: (1) the circularity paradigm relies on information that incurs its own associated costs, and (2) it is bound by the second law of thermodynamics, which states that energy is inevitably lost in the form of waste heat during any energy conversion process. Thus, it should be complemented by other environmental frameworks. It is clear that there will always be consumption of materials and energy, which leads to the depletion of shared resources regardless of the degree of circularity within the economic paradigm.

Therefore, for our purposes, the impact of any economic system in terms of energy, raw materials, and emitted pollutants can be primarily quantified by evaluating its energy expenditure, which largely contributes to the depletion of shared resources in terms of both quality and quantity, as depicted in Figure 1. This remains true even when resources are completely recycled (in a fully circular economy). While it is not feasible to achieve complete resource recycling, it holds true to a certain extent, but the introduction of measuring common raw materials, tracking energy consumption, and evaluating their impact is becoming increasingly viable in today’s technological landscape. Notable advancements in energy trading systems and smart-metering hardware enable more-accurate LCA estimates in the food production sector [18]. This opportunity allows the allocation of users’ energy consumption to respective sets of pollutants derived from the impact assessment analysis of the energy source utilizing various approximation criteria [19,20].

3.2. Framework Boundaries

Required raw materials, energy exchanges, processes, and measurements are considered all along the life-cycle of products and services [21]. Due to lack of data, usually at the product and service level, it is common practice to refer to data of similar products or similar trade-offs. Nonetheless, an ad hoc analysis would be ideal, and an opportune incentive to foster its adoption is necessary for a truly sustainable society. Examples of best practices are certifications such as the Environmental Product Declaration (EPD), which defines Product Category Rules (PCRs) (a set of standards for sampling and analysis that needs to be performed for each specific product category in accordance with its composition or processing; also called single-company product-specific EPD, https://www.environdec.com/pcr-library, accessed on 23 May 2023)). In particular, categories of criteria concerning the EPD standards are provided in

Table 1. EPD categories.

Criterion	Unit
Acidification	Kg SO2eq
Eutrophication	Kg PO4-eq
Global warming	Kg CO2eq
Depletion of stratospheric ozone layer	Kg CFCeq
Formation of tropospheric ozone	Kg Ethyleneeq

. Impacts, particularly from an LCA perspective, must refer to a gate-to-gate modelling approach, and therefore their allocation is only due to their respective stakeholder’s activity.

3.3. LCA Tree: Allocation, Aggregation, and Cut-Off

As previously introduced, each activity processes materials and energy to produce value in the form of finite products and services. Each activity, accounting for GHG protocol Scope 1, is responsible only for its direct contribution to the depletion of the environment. This means that at the Scope 1 level, a company that emits 3 Kg of CO_2eq is only responsible for such an amount of CO_2eq. Meanwhile, at the Scope 2 level, the amount of emissions generated by the considered activities is allocated to its energy production process. At the Scope 3 level, the amount of Scope 1 is incremented by the CO_2eq emitted through all the prior activities supplied to the service of such company, including the supply of energy. This framework defines a multivariate approach, with features defined in Section 4.1.3 and Section 5.1 and better expanded in Appendix A.2 and Appendix A.4. This approach allows for a comprehensive and scalable scope definition, upgrading the current Scope 1, Scope 2, and Scope 3 categorization of the International Panel on Climate Change (IPCC) as well as other scopes as per those of the Global Reporting Initiative (GRI or EPD).

3.4. Impact Standardisation

Currently, LCA studies are conducted using various boundary conditions, such as gate-to-gate, cradle-to-gate, and cradle-to-grave, which refer to different stages of the supply chain. The absence of standardised metrics (e.g., EPD, ReCiPe, IPCC, and other protocols) and the inherent challenges in achieving single-score aggregation while maintaining the comprehensiveness of results are limiting factors for the development of robust models. In this paper, we limit our discussion to a single-score aggregation by using as an example the EPD standard with gate-to-gate boundary conditions. The values of each impact category should be standardised, either with the Z-Score (through statistical properties such as average and standard deviation) or $minmax$ normalisation (through the absolute maximum and minimum values in the whole dataset). Normalisation allows us to compare different patterns of signals whose magnitude may otherwise differ in size. Given that elicitation of weights and identification of the criteria are part of the decision-making and political process, for the sake of easier understanding, each weight will be set to 1 for each of the five criteria of the EPD certification.

4. Theory

In this section, the combination of the Energy Theory of Value is introduced to blockchain systems under an LCA perspective.

4.1. Energy Theory of Value

Starting from the commons introduced in Section 2, we will evaluate the surplus value that can be harnessed from raw resources and the externalities affecting their appreciation by any activity and, more broadly, by society. The idea is to shift the current economic evaluation to a valorisation that can create economic value for society more effectively [22]. To this purpose, we first associate the surplus value that can be extracted from common resources to the ability of society to generate productive means; then, we bridge these productive means to capital. This approach is inspired by physiocrats such as Quesnay and Turgot [23], who examined agricultural practices, and, more recently, by Serrano who examined non-agricultural productive activities [24]. With this approach, we contextualise the political economy aspects discussed in the formulation of the power theory of value by Nitzan and Bichler and lay the basis for an accounting of nominal (exchange value) vs. actual values (use value) [25]. We neglect the adoption of a financialisation based on capital as non-physical power (e.g., decisional dependence), as considered by Nitzan and Bichler, and rather focus on its physical features through implementation of the Energy Theory of Value to outline a new financialisation paradigm inspired by the previous work of several scientists in the last decades [26,27,28,29]. Particularly, we combine the trade value with the pleasure value, as defined by Stallinga [30], by grouping them in a subjective value form (e.g., use value) so as to extend the notion of mere physical quantities and overcome the limitation of net energy amounts as a policy planning tool—as pointed out by Hyman [31]. At the basis of this rationale, we consider that exergy represents a qualitative feature of energy transfer potential, as inspired by the previous work of Szargut in the indicator for cumulative accounting along supply chains [32] and Ayres for the bi-dimensional mapping of physical (energy/material) and subjective (value) dimensions [33].

4.1.1. Capital (Energy) and Debt

Considering physical commodities, we can harvest and transform existing reservoirs, which, in certain cases, are continuously renovated by external sources. The most important contribution to commons comes in electromagnetic form from the Sun. Natural storage of this constant flux may occur in multiple forms, mainly chemical (transformation of solar energy into cellulose by vegetable livestock and long-term creation of natural gas, coal, and oil reserves), thermal (heating of the atmosphere through its heat capacity, storage of charged energetic particles in the Van Allen belt, and heating of the oceans and landmass through their heat capacity), and kinetic (atmospheric circulation, thermo–haline circulation, water cycle, etc.). Another contribution comes from the radioactive decay of minerals that are thought to generate geothermal potential. Other common resources that can only be depleted are raw materials, such as technological metals and other elements such as rare earths, lithium, semiconductors, noble gases, and so on. Although accounting for the amount of energy stored in all raw materials retrievable on Earth could better help us understand the amount of surplus of wealth that can be potentially generated, let us disregard such a balance by justifying this choice with the fact that energy is needed in any case to extract and transform raw resources to make them suitable either for usage or for further energy extraction. The maximum ready-to-use energy availability (and therefore capital such as human labour, machinery, and commodities) that an economy can create while keeping its energy balance positive can be roughly approximated with the incoming solar net energy on Earth’s surface. Given the average value of 340 W/m² estimated by NASA [34] and a surface of approximately $5.1\times {10}^{14}$ m², the incoming power results to be approximately 1.7 PW; therefore, it is around 2 ZJ of energy per year ($1\mathrm{Z}={10}^{21}$). This amount could be trimmed to match the effective land surface of Earth, or fractions of it, but we feel that land-use allocation and technological capabilities remain a political decision, so we set the limit at a conceptual level at this stage. Although all this power may be potentially retrievable, technological and economic trade-offs imply a different reality. Nonetheless, this amount will be referred to as the maximum amount of energy that can be potentially used every year by a sustainable society. Therefore, as introduced in Section 5.2.2, the maximum amount of issuable debt that can be sustainably generated on Earth cannot be higher than the overall energy potentially consumed.

4.1.2. Use Value and Exchange Value

The statement that machines and humans produce a different quality of work is colliding nowadays with the increase in flexibility and efficiency of Artificial Intelligence (AI)-powered systems. The usage of statistical and logical methods for simulating complex decision-making capabilities has nearly approached the level of creativity that humans possess.

Energy is, regardless, always required. Considering that labour is a transformed form of energy, humans as biological thermodynamic machines, and a colliding relation between exchange value and use value [35], let us take the use value for the accounting of externalities affecting the utility of purchasers [36]. In this analysis, behavioural science will not be discussed in detail; therefore, let us consider this function as a random variable for now. Let us also justify this approximation by stating that information retrievable—for a high number of stakeholders—allows us to consider it only as white noise with known statistical properties. Part of the scope of this paper is to allow the gathering of the metrics for a heuristic assessment of the distribution of such statistical properties at an operational stage. Furthermore, let us define the resultant benefit that drives the feasibility of a transaction for the buyer as the maximum between the exchange value of the activity (ex_v) and its subjective value (use_v) for the buyer:

$$\begin{array}{c}\hfill benefit=max(us{e}_v,e{x}_v)=\\ \hfill max(f\left(U_{i,j}\right),O_{i,j})\end{array}$$

(1)

where f(U_i,j) is a function of the personal needs of the stakeholders towards the activity (or a set of activities) i purchased from stakeholder j. O_i,j is the exchange value of the activity, and U_i,j is the utility of the buyer towards the output generated by the activity of the considered seller.

4.1.3. Measuring the Exchange Value

Based on the work of Szargut and Morris [37], we use the CExC to be able to account for raw materials and energy and for the perfection of production processes leading to the activity (or supply chain) under consideration. The value of the activity can be expressed as the energy consumed ($E_{i,j}$) plus the value of the materials processed by the stakeholder to create a certain output of its activity ($C^{H\left(N\right)}$), where H represents the set of extracted flows from the common reservoirs for a degree of depth of the analysis N.

$$O_{i,j}=C_{i,j}^{H\left(N\right)}+E_{i,j}$$

(2)

The wealth $O_{i,j}$ in this case represents the exchange value of the produced output, which is an objective and quantitative reference of value. However, in addition to this value, we need to consider the subjective difference in cost or benefit, as defined in Equation (7), which relates to the value of the common as defined in Equation (1). The customers’ willingness to pay will also depend on the environmental externalities generated by each activity, which, in our first approximation, are captured through a custom utility function that is dependent on socio–economic aspects. The internalization of these social costs will be discretised in a time unit and referred to as the Stakeholder’s Responsibility Paradigm (see Section 5.1), considering that stakeholders as well as industrial policymakers fully internalize them as a cost upstream from their supply chains. For now, let us continue deriving the dependence of activities on each other and on the raw materials and resources stakeholders process and extract. Using Equation (2) and knowing that the value of the commodities in input C^H(N) is the weighted composition of the value of commodities purchased by the supplier plus the energy consumed to process them and proceeding recursively until extraction from the reservoir of the considered resource (e.g., $N=\infty$), we can define the energetic cost as:

$$E_{i,j}={\mu }_{i,j}{\varphi }_{i,j}{\psi }_{i,j}{C}_{i,j}$$

(3)

where:

$$\begin{array}{cc}\hfill & {\mu }_{i,j}=\mathrm{efficiency}[\%]\hfill \\ \hfill & {\varphi }_{i,j}=\mathrm{compound}\mathrm{exergy}[\frac{kWh}{K{g}_{C_{i,j}}}]\hfill \\ \hfill & {\psi }_{i,j}=\mathrm{energy}\mathrm{price}[\frac{\mathrm{EUR}}{kWh}]\hfill \end{array}$$

(4)

The term Kg_Ci,j in Equation (4) is the weight of the resource used by j to produce activity i. Equation (2) can be rewritten as the summation over all users belonging to the tree of the transaction (p) considered; we drop the index j for simplicity:

$$O_i=\sum _p^{\pi (N=\infty )}{\mu }_p{\varphi }_p{\psi }_p{R}_p$$

(5)

where:

$$R_p=R_p^{*}[\frac{kg}{m^2·yr}]·A_p\left[m^2\right]·\Delta t_i\left[yr\right]$$

(6)

The term $C_{i,j}$ in (4) is now substituted by the term $R_p$, which represents the intake from common resources R as specified in (7). Equation (5) represents the relation between the exchange value of an activity to all the activities it depends on and the raw resources dispersed over an area A, whose depletion occurs during a time $\Delta t$. The term $\pi$ stands for the whole set of transactions limited by a degree of depth, or accuracy, N; while p stands for the current level of the transaction tree being accounted for. Both sizes will be further described in Section 5.1. The density of the extraction capabilities of the raw materials over a considered area is defined via Equation (6), where the surface and time units have been expressed in $m^2$ and $yr$, respectively. It is worth pointing out that this cumulative expression of energy exchanges, represented in the form of embodied wealth, is already integrated along time units through the term $\Delta t$. This formulation will be re-contextualised in (11).

4.2. Evaluation of Societal Wealth

This paragraph introduces the theory for evaluating societal activities, which mirrors the quantifiable energy metrics previously introduced and contributes to defining the objective wealth of an economy.

4.2.1. Quality of the Common

The quality of activity output (and of the resources) is an important concept in the proposed theory. It does not only identify a part of the utility any user can perceive from a certain activity, as defined in Equation (2), but also its physical properties that affect the utility of its usage and, therefore, the subjective value component of any stakeholder. The “quality” of a stream of by-products (waste matter/energy released into the environment by any activity, waste heat being the biggest energy flow mankind is generating, as big as 142 PWh/y [38]) represents the availability of harnessed energy therein contained. In the context of stakeholders or species interacting with toxic, carcinogenic, or mutagenic compounds dispersed into matter or energy flows, the concept of exergy in thermodynamics can be used to represent the potential energy contained in such streams of by-products [39]. This qualitative feature of matter and energy, which represents also the potential to retrieve energy from materials or to cause harm to the same stakeholder, is mapped according to the availability of users to pay for the activity that creates said feature. As exergy represents the entire potential of energy state changes in relation to an environment that normally provides lower grade enthalpy for exchanges, so stakeholders’ utility functions represent wealth changes of the seller due to the utility of their products to the buyer (both for products, waste, and irreversible waste that cannot be recovered, such as dispersed emissions) in relation to both system states.

Let us refer to this resulting economic outcome of the quality of output as commons depletion/restoration due to the modification of the intrinsic value of the resources or, economically speaking, to the externalities that the considered activity creates in the appreciation of such value.

4.2.2. The Value of the Common

What would be the value of a resource without any living creature being able to use it? In the specific case of Intellectual Property (IP) issues, the resources required for knowledge production are a form of energy, such as food, electricity, information, and experiences, that bring inventors and innovators to a certain result. It is energy that generates information. IP value is not only the energy required to produce it nor its agreed material value; rather, it is a subjective expression of the buyer. Therefore, without stakeholders producing added value through exchanges (activities) and depleting the resources, there would not be any initialization of such a concept to account for. As such, in this model, all resources will be ordered according to stakeholders’ use: in other words, purchase transactions rather than in a physical spatio–temporal locus. Let us call C the value of the common, and R is the value of all raw materials extracted for the production of the activities of stakeholders j belonging to ensemble J, all of them involved in transaction i, which is part of the total set of transactions I. Let us continue to add to the resources used for common economic purpose the difference between the subjective value perceived by the purchaser and the (energetic) production cost of the considered transactional value exchanged by the seller. The dynamics defining this term will be represented by the demand and production cost curve of the stakeholders involved in the transaction through the relation expressed in Equation (1). The value of the common will be the value of all raw materials possessed (resources extracted) by these stakeholders plus the subjective value of all stakeholders generated by the exchanges of such goods:

$$\begin{array}{cc}\hfill & \hfill C=R+\sum _{i,j}^{I,J\in \mathsf{\Pi }}max(us{e}_v^{i,j}-e{x}_v^{i,j}[-P_{i,j}],0)\hfill \\ \hfill & \hfill us{e}_v^{i,buyer}=f(U_i^{j,buyer})\mathit{see}\mathit{Equation}\left(1\right)\hfill \\ \hfill & \hfill e{x}_v^{i,seller}=O_i^{seller}\mathit{see}\mathit{Equation}\left(2\right)\hfill \end{array}$$

(7)

The term $\mathsf{\Pi }$ stands for the transaction tree of the whole system and embraces the set of all transaction trees $\pi$, as defined in (5). P_i,j represents the impact cost created by the seller j of the considered transaction i. This term is surrounded by square brackets to indicate that it can be considered only if the cost relative to the impacts under analysis can be quantified. In case the cost of such impacts cannot be quantified (e.g., due to the complexity of modelling pollution, the complexity of toxicology dynamics, etc.), the term $P_{i,j}$ is accounted as affecting the utility function of the term $us{e}_v^{i,j}$. This is equivalent to considering information on pollutants affecting purchases while disregarding the spatio–temporal locus, considering, therefore, that pollutant emission influences stakeholders and their transactions ubiquitously and instantaneously. The removal of noxious emissions is considered a negative social cost, while its injection into the environment is a positive social cost. This choice is due to the harmful effects on living beings of noxious compounds [40]. The hypothesis behind this assumption is that externalities occur because production is satisfied by the demand (that creates the transaction), whose value represents the value subjectively perceived by the buyer. No assumptions are made about the properties of the commons: the framework we use must be seen from a holistic point of view—that of the ecosystem.

4.2.3. Evaluating the Costs of Impacts

The utilitarian internalisation can be considered, as defined in Section 4.2.2 (omitting the impact term and considering the ubiquitous and immediate knowledge of stakeholders), in case it is not possible to describe how pollutants can affect the economies of society, either due to the lack of markets for a specific pollutant or due to the lack of modelling and sampling schemes. Let us introduce the theory for evaluating tradeable pollutants whose impacts and costs can be quantified. We define the monetized impact P_i,j, referring to an activity i operated by the stakeholder j, as:

$$P_{i,j}=p_{i,j}\tau C_{i,j}$$

(8)

where:

$$\begin{array}{cc}\hfill \tau & =\frac{\mathrm{EUR}}{S.I.U.}=\mathrm{impact}\mathrm{value}\hfill \\ p_{i,j}\hfill & =\frac{S.I.U.}{k{g}_{i,j}}=\mathrm{impact}\mathrm{unitary}\mathrm{rate}\hfill \end{array}$$

(9)

and:

$$C_{i,j}=C_{i,j}^{*}\left[\frac{kg}{m^2·yr}\right]·A_{i,j}\left[m^2\right]·\Delta t_i\left[yr\right]$$

(10)

The relationship between quantity and externalities is assumed to be linear. It can be also described by higher-order functions, though this implies higher computational costs for the identification of sustainable policies. In case this framework is applied only to CO_2eq, $\tau$ refers to the carbon market price. In case of multiple impacts instead, the weighting of the criteria will be calculated according to the standardisation defined in Section 3.4. In case of a positive impact to or mitigation of resource extraction, the regenerative rate or depletion rate can be accounted for as an impact once the values and the amounts of the resource regenerated or depleted are known. The figures introduced in the previous equations are listed in

Table 2. Variables and units.

Symbol	Unit
t	Stakeholder
N	Threshold
j	Supplier
Oi,j	€
Ei,j	€
Ci,j	Kg or m3 s.c.
Pi,j	€
pi,j	Standardised impact
	unit (S.I.U.)/Kg
	Table 1; Section 3.4

.

4.3. Time-Discrete Activities, Overcoming a Lack of Raw-Material Tokenization

For example, let us consider Bob buying a product from Alice. By knowing only the impact of Alice’s operation and assuming Alice will provide different products/services as the output, we cannot allocate the impact of inputs (resources such as energy, input commodities, and raw materials) and processing labour for only the product purchased by Bob. Therefore, this sustainability protocol must be integrated with ERP (Enterprise Resource Planning software) data in order to operate at the product/service level, otherwise the economic canvas can be redistributed only at the stakeholder level. Nonetheless, we can consider impact and resource responsibilities related to the overall set of products/services produced in a time unit $t-t_0=\Delta t_{sys}$, defined as the time-step for the economic protocol, by a stakeholder j, and considering energy expenditures of the same stakeholder in the same time unit, expressed by the following relation:

$$\begin{array}{cc}\hfill E_j=& \sum _{t_0}^t\frac{E_{i,j}}{\Delta t_{sys}}\hfill \\ \hfill P_j=& \sum _{t_0}^t\frac{P_{i,j}}{\Delta t_{sys}}\hfill \\ \hfill C_j^{H\left(N\right)}=& \sum _{t_0}^t\frac{{\sum }_p^{\pi (N,j)}{C}_p^H}{\Delta t_{sys}}\hfill \end{array}$$

(11)

By doing so, we will be able to discretize differences among stakeholders’ transactions and processing intervals and the pace of the system’s accounting for transactions. For the sake of clarity, we dropped the subscript j in Equation (11), which represents the stakeholders whose activities belong to the transaction tree of stakeholder j.

5. Results

In this section, the conceptual results obtained by application of the protocol defined above are shown. For clarity, we recall that the definition of the protocol is the combination of Equations (2), (3), (5)–(8), and (10) and the boundary conditions defined in Figure 1. The result is a system represented in Figure 2, where cylindrical shapes are entities of the protocol and trapezoidal shapes are processes that upgrade the entities of the protocol. The Social Cost Compensation in Figure 1 is now called Compensation and is provided thanks to stakeholders that spend energy to compensate for the cost of energy consumption of other stakeholders in an energetic, material, or informational form. Further implications of the results discussed hereafter can be found in the Appendix A, Appendix B and Appendix C.

The synchronisation process among nodes is therefore a transparent standardisation process of LCA locally validated data. We are now going to discuss more detailed implications.

5.1. A Tool for Industrial Policies: Stakeholder’s Responsibility Paradigm

Considering a policymaker whose intention is to evaluate the industrial strategy that creates more wealth for a certain set of stakeholders at a certain level of the supply chain, from a given stakeholder, proceeding upwards until level N in the procurement of energy and input materials required for the activity of any stakeholder over the time unit $t-t_0$, the wealth balance of a $j^{\mathrm{th}}$ stakeholder can be defined by the following equation:

$$O_j=\underset{Resources}{\underbrace{C_j^{H\left(N\right)}}}+E_j-\underset{N^{\mathrm{th}}scope}{\underbrace{\sum _{p\left(j\right)}^{\pi (N,j)}{P}_j^{j,j+1}}}$$

(12)

which derives from Equation (2) and the social cost defined in Equation (7). The cost of externalities is hidden by the producer, therefore resulting in a decreased value of the produced wealth. The O stands for Output and is represented by a monetary size, while C represents the commodities in the input, as a sum of the withdrawn value of all raw materials H for the considered degree of accuracy of the impact assessment N. P is the impact generated, represented as wealth depletion. The sum on the right-hand side refers to the transaction level of stakeholders. Particularly, superscript j, j + 1 refers to the stakeholder at level j to the stakeholder at level $j+1$, where j purchases from $j+1$. N represents the degree of accuracy of the impact allocation threshold, further explained in Appendix A.2, while $\pi$ is a subset of $\mathsf{\Pi }$ introduced in (7). Particularly, j is the counter for the depth-level of the analysis (at the transaction/procurement level); this refers to the considered stakeholder whose impact is being cumulated. The grouped references Resourcesand N^th scope in Equation (12) represent, respectively, the Resource and the N^th-scope component of this model. Introducing the definition of the wealth from raw materials (R) from Equation (5) and substituting it into Equation (12), we can write:

$$O_j=\sum _{p\left(j\right)}^{\pi (N=\infty ,j)}{\mu }_p{\varphi }_p{\psi }_p{R}_p-\sum _{p\left(j\right)}^{\pi (N,j)}{P}_j^{j,j+1}$$

(13)

Stating that the value of the overall activities produced by stakeholder j in the time span $t-t_0$ is equal to the algebraic sum of the energy produced for the extraction of all the required resources, both for electricity production (e.g., energy carriers such as coal or uranium) and raw materials (e.g., water and minerals) consumed in the interval $t-t_0$ minus the sum of the impact the stakeholder is responsible for, which affects its supply chain upstream. Please note also that the energy produced is proportional to the value of the energy exploitable from the compound and to the price at which such energy can be sold to the market, as well as to the efficiency of the transformation. For raw material extraction, it is the maximum of the energy retrievable from raw materials (an example can be found in [41]) and the minimum amount of work required to extract such resources [42]. In this context, the coefficients $\mu ,\psi ,\varphi$ can be considered as weighted averages of the respective sizes of all process-specific activities generated by stakeholder j in the time unit $t-t_0$.

5.2. Grouped Interests

In this section, the theory for the accounting of grouped stakeholders pursuing a joint strategy is discussed. The group of stakeholders can be any conglomerate of decision-making votes. It can be represented by countries, local communities, or even by blockchain nodes. Each member of any of these groups is thought to contribute to the political vote of the group according to the criterion defined by the group (e.g., the land each user’s activity insists on, the energy consumed, etc.; see Section 5.4).

5.2.1. Wealth of Territories

By territories, we mean not only countries, but also any land extension where labour exists as a part of an economy that processes resources through a network of value exchanges based on interests. From aggregation of the user value for all users J within a certain territory z, the wealth balance of the territory can be written as follows:

$$\begin{array}{c}\hfill O_z=\sum _j^{J\left(z\right)}{O}_j=\\ \hfill C_z^{H\left(N\right)}+\sum _j^{J\left(z\right)}{E}_j+\sum _j^{J\left(z\right)}{P}_j\end{array}$$

(14)

where $C_z$ stands for all commodities produced in the territory z from the needed set of raw materials H and for a cutoff N of the protocol. The summations $E_j$ and $P_j$ stand for all energy expenditures and respective social costs generated by the stakeholder j. Please note that in the above equation, the $N^{\mathrm{th}}$ scope term is not present, compared to the stakeholder’s responsibility paradigm (see Section 5.1). Given its subjectivity, in terms of stakeholder’s perceived value, is not necessarily reflected in an objective impact on society, therefore the term f(U), as defined in Equation (1), is omitted. We note the fact that the countries distribution is not necessarily based on a territory map: federations of countries where goods (or services, or information) are produced and transported can still offer a sustainable solution, thus keeping well-balanced wealth and accommodating for local unbalances globally.

5.2.2. Land Use Issuance Cap

As introduced in Section 4.1.1, irradiance being equal to $\gamma$ and considering ideal efficiency to transform energy into work, the maximum amount of debt we can issue per country z can be given by the following equation:

$$\begin{array}{c}I\left(O_t\right)=\hfill \\ max(O_t^z,{\int }_{A,t}\frac{\gamma \psi A}{A_{tot}}dtdA)\hfill \end{array}$$

(15)

where $\psi$ is the price of electric energy, and $O_t^z$ is the total value produced by all activities of the stakeholders in the country z during the time interval t. The irradiance value, $\gamma$, is dependent on time, latitude, and longitude of the country from a first-order approximation and is, therefore, integrated along the time and the surface of the country. The term $A/A_{tot}$ is a redistribution coefficient to account for differences in area extension of lands of different communities, with $A_{tot}$ being the total surface of the globe. Different criteria could also be defined, for example, by including other external indicators or weighting the irradiance with a redistributive coefficient.

5.3. Sustainable Minting Function: Keen Universal Basic Energy

The green arrows in Figure 3 represent the energy purchased or sold by different stakeholders and, therefore, the credit (+) or debit (−) they get rewarded with for the potential they have in creating value to society from such energy exchanges. Let us name this concept Keen Universal Basic Energy (KUBE) in honour of the person who first introduced this term. Through this instrument, users are provided with an amount of currency unit proportional to the energy consumed and to the inverse of its impact (more complex indicators can be defined for the KUBE function).

$$\left\{\begin{array}{cc}{\mathsf{€}}_i=\frac{|E_{i,j}|}{P_{i,j}}\hfill & \hfill \mathrm{if}P>0\hfill \\ {\mathsf{€}}_i=-\frac{P_{i,j}}{|E_{i,j}|}\hfill & \hfill \mathrm{if}P\le 0\hfill \end{array}\right.$$

(16)

It is worth noting that each of the represented stakeholders can take the role of an energy banking supplier. This framework is intended to facilitate the sustainable adoption and extension of carbon credit schemes and other impact assessment criteria to activities that not only produce energy, as is currently the case, but also any other product or service. Considering this system as a payment network, accounts are expected to have a unique identifier or address, which might be related to sensitive information required by local jurisdictions. The anonymity of stakeholders is of paramount importance in the adoption and acceptance of any payment system: privacy concerns can be addressed by clear rules embedded into an appropriate minting function, which can also allow analysis of economic flows for scientific research purposes.

5.4. Node Consensus for Pollution Sampling Schemes

Impact aggregation to a single score is necessary to compare and rank activities for distributing financial incentives. This process is very sensitive to the weight of parameters used for the aggregation. In this paper, integer values for all indicators are considered as defined above by the EPD standard. Nonetheless, particular aggregation rules can be specified after geographical, social, and environmental economic analysis for the attainment of specific targeted needs of reduction or increase for any criterion (e.g., see Section 3.4). This process can be implemented through different nodes that interact with each other automatically to manage the conversion of different weights and criteria adopted in a transparent manner.

5.5. The Role of a Payment Tracking System in the Depletion of the Commons

Let us consider two companies that own land located above a natural water spring (having neither a well nor smart metering hardware), and let us suppose that the local legislation allows these companies to be entitled to exert full control over the raw materials located on their property. Now let us suppose both companies decide to bottle natural water, collecting it from two natural springs, and both are harvesting it from the same groundwater reservoir. Satellite imagery may provide an effective tool for assessing water withdrawals for better planning of water reservoirs [43]. However, discriminating stakeholders’ individual consumption for nearby withdrawals from the same reservoir may be challenging. Therefore, tracing the amount of water harvested from the environment by focusing on monitoring the event of water depletion while maintaining spatio–temporal resolution is not as effective as focusing on anonymous stakeholders’ behaviour in known locations would be.

5.6. About Extractors, Recyclers, and Optimizers

Any activity that mitigates resources consumption can be given a special role. The laws defining the energetic costs and the impact of the regeneration activity for any resource are defined the same for the energy providers, particularly as per Equation (3). Considering a set of stakeholders inside an approximated supply chain, as shown in the diagram of Figure 3, the extractors are set in red and the blue arrows stand for the wealth generated due to resource restoration. We might consider particular activities that are conceived to optimize or reduce expenses under special circumstances while processing and consuming energy. For example, climate change has statistically increased the likelihood of dealing with floods, droughts, and storms. Investing money and energy to spatially reconfigure raw materials is a way to optimize resources without necessarily depleting them in order to reduce the risk of disasters, which would imply much higher reconstruction costs. Scientific research and the IP it produces also belong to an optimization problem characterized by unknown spatio–temporal scopes: constant investments will generate, randomly, optimization of resources, new stakeholders, new resources to be depleted, and new energy channels to utilize.

6. Conclusions

In conclusion, we have shown a possible way of tackling sustainability challenges nowadays. These being a widespread adoption of sampling of externalities and the integration of impact assessment into economics, we have proposed a methodology for integrating such two aspects, overcoming the concerns of confidentiality of supplier information with anonymous stakeholder reward systems. Particularly, the adoption of public blockchain databases enables transparency of scientific research over a persistent data layer and promotes progress in standardisation through heterogeneity of interconnected domains. Bottom-up ingestion and withdrawal of credit from energy consumption (data retrieved from electricity consumption metering devices) were discussed as approaches to avoid wealth-distribution issues. An initial model framing resource-based economics was proposed in the context of the blockchain protocol, where model constraints are defined as energy retrievable per land-use allocation. A set of general criteria for impact assessment of resource depletion and pollutants was proposed as a component to facilitate, in their simplicity, global scalability and adoption and to create intrinsically sustainable economies whose sustainability impact is digitized at the transaction time and persists in a globally accessible protocol. Finally, we have discussed the political need to incentivize sampling and standardisation of impacts among all stakeholders in a supply chain as a condition towards comprehensiveness and adoption of sustainability frameworks.