Skip to main content
  • Loading metrics

Optimization and decision support models for deploying negative emissions technologies

  • Maria Victoria Migo-Sumagang,

    Roles Data curation, Formal analysis, Funding acquisition, Software, Validation, Visualization, Writing – original draft

    Affiliations Department of Chemical Engineering, De La Salle University, Manila, Philippines, Department of Chemical Engineering, University of the Philippines Los Baños, Laguna, Philippines

  • Kathleen B. Aviso,

    Roles Formal analysis, Methodology, Project administration, Software, Supervision, Validation, Writing – review & editing

    Affiliation Department of Chemical Engineering, De La Salle University, Manila, Philippines

  • Dominic C. Y. Foo,

    Roles Funding acquisition, Project administration, Supervision, Visualization, Writing – review & editing

    Affiliation Department of Chemical and Environmental Engineering/Centre of Excellence for Green Technologies, University of Nottingham Malaysia, Selangor, Malaysia

  • Michael Short,

    Roles Funding acquisition, Project administration, Supervision, Visualization, Writing – review & editing

    Affiliation Department of Chemical and Process Engineering, University of Surrey, Surrey, United Kingdom

  • Purusothmn Nair S. Bhasker Nair,

    Roles Data curation, Funding acquisition, Visualization, Writing – review & editing

    Affiliation Department of Chemical and Environmental Engineering/Centre of Excellence for Green Technologies, University of Nottingham Malaysia, Selangor, Malaysia

  • Raymond R. Tan

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    Affiliation Department of Chemical Engineering, De La Salle University, Manila, Philippines


Negative emissions technologies (NETs) will be needed to reach net-zero emissions by mid-century. However, NETs can have wide-ranging effects on land and water availability, food production, and biodiversity. The deployment of NETs will also depend on regional and national circumstances, technology availability, and decarbonization strategies. Process integration (PI) can be the basis for decision support models for the selection, planning, and optimization of the large-scale implementation of NETs. This paper reviews the literature and maps the role of PI in NETs deployment. Techniques such as mathematical programming, pinch analysis (PA), process graphs (P-graphs), are powerful methods for planning NET systems under resource or footprint constraints. Other methods such as multi-criteria decision analysis (MCDA), marginal abatement cost curves, causality maps, and machine learning (ML) are also discussed. Current literature focuses mainly on bioenergy with carbon capture and storage (BECCS) and afforestation/reforestation (AR), but other NETs need to be integrated into future models for large-scale decarbonization.

Author summary

Radical approaches will be needed to deal with the ongoing climate crisis. In addition to the reduction of greenhouse gas emissions through strategies such as energy conservation or decarbonization of electricity, negative emissions technologies (NETs) that remove carbon dioxide from the atmosphere will also have to be commercialized. These technologies can offset both historical greenhouse gas emissions as well as residual emissions from sectors that are inherently hard to decarbonize. However, the rapid scale-up of NETs poses the risk of unintended consequences due to their need for energy, land, water, nutrients, and other resources. These requirements also translate to incremental cost and social acceptability aspects of carbon drawdown options. The evaluation of many alternatives is also problematic due to the uncertainties inherent in new technologies. This paper surveys the emerging literature on decision support models that have been developed to deal with these issues and facilitate the large-scale deployment of NETs.


Negative emissions technologies (NETs) will be needed to achieve the global net-zero emission target by mid-century [1]. NETs remove CO2 from the atmosphere and transfer it to other physical or biological compartments [2]. The drawback of NETs is the potential effects on biogeochemical cycles that lead to adverse environmental impacts [3]. The scale and timing of NETs deployment will depend on the regional and national circumstances, technology availability, and level of decarbonization of different sectors [1]. Hence, computer-aided decision support for NETs deployment is an important research area. Process integration (PI), a branch of process systems engineering (PSE), offers various methods for such applications.

PI is defined as “a holistic approach to design and operation that emphasizes the unity of the process” [4] and focuses on the efficient use of resources and the reduction of pollution [5]. Two groups of techniques developed in PI are pinch analysis (PA) and mathematical programming (MP) [5]. PI techniques were first used for heat recovery system design in process plants [6]. Mass integration was later developed by capitalizing on the structural similarity of heat and mass transfer [7]; PI principles were extended to CO2 emissions reduction through carbon-constrained energy planning with tools such as carbon emissions pinch analysis (CEPA) [8]. Combining PI with artificial intelligence (AI) is also a promising area [9].

PI models can play an important role in high-level policy decision analysis to fight climate change [10]. Integrated assessment models (IAMs) remain critical in emissions reduction analysis, but they should be supplemented with other modeling approaches [11]. In particular, there is a need for optimization models to prescribe normative courses of action rather than merely describing the results of predefined scenarios [12]. PI models can therefore supplement IAMs in planning the large-scale implementation of NETs.

A keyword map based on a Scopus database search (using the search terms “negative emissions” or “carbon dioxide removal” and the techniques, “optimization,” “mathematical programming,” “linear programming,” “pinch analysis,” “marginal abatement cost,” “p-graph,” “multi-criteria decision analysis,” and “machine learning” until the year 2022 is shown in Fig 1. The sizes of the nodes indicate the frequency of occurrence. Five thematic clusters of topics based on content [13] can be seen in different colors, namely, carbon dioxide removal and optimization (blue), climate change and GHG (red), carbon capture and biomass bioenergy (green), negative emissions (yellow), and life cycle assessment (LCA) (violet). The keyword “process integration” is found under the red cluster but is linked with all the other thematic clusters, indicating that PI is an emerging topic that warrants attention.

Fig 1. Keyword map of the literature on the optimization and decision support techniques for NETs deployment.

Each node represents the keywords that have occurred at least 5 times in the literature, based on a Scopus database search (using the search terms “negative emissions” or “carbon dioxide removal” and the techniques, “optimization,” “mathematical programming,” “pinch analysis,” “marginal abatement cost,” “P-graph,” “multi-criteria decision analysis,” and “machine learning”). The search yielded 150 results (excluding articles from unrelated fields). The bigger the size of the node, the more frequent the occurrence of the keyword. NET, negative emissions technology.

Early literature reviews on NETs assess their technological readiness, costs, and carbon dioxide removal (CDR) potentials [14]. The biophysical limits of NETs were also evaluated based on multiple footprints [15]. A comprehensive report evaluated NETs based on costs and potentials as well as co-benefits, social acceptance, implementation barriers, and readiness [2]. A three-part review series that emphasized the importance of NET portfolios assessed the research landscape [16], costs and side impacts [17], and commercialization prospects [18]. NETs have been evaluated using the United Nations Sustainable Development Goals (UN SDGs) [19] and LCA [20]. Despite the extensive literature on NETs, no reviews have been found on the optimization and decision support models for their deployment.

This paper addresses this gap in the literature by giving a state-of-the-art survey of the optimization and decision support models for NETs, with an emphasis on PI-based techniques. The rest of the paper is organized as follows. The next section gives an overview of NETs. Subsequent sections discuss the different modeling approaches and applications in NETs depending on the task. The final section gives the conclusions and future research outlook.

Overview of negative emissions technologies

Early discussions on engineered CDR date back to the 1970s [21]. Climate change is now recognized as a global crisis that requires gigaton-scale solutions [22]. The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) discussed 2 NETs, namely, bioenergy with carbon capture and storage (BECCS) and afforestation/reforestation (AR) [23]. The term CDR appeared in the IPCC’s Fifth Assessment Report (AR5) wherein NETs were included in the new GHG emissions mitigation scenarios [24]. In AR5 scenarios, higher emissions lead to greater reliance on NETs after 2050 [24]. IAMs that simulate the global GHG emitting systems use NETs to make feasible scenarios and reduce system costs [25]. The first installment of the Sixth Assessment Report (AR6), “The Physical Science Basis,” stated that NETs have the potential to remove carbon from the atmosphere for storage in reservoirs, reverse ocean acidification, offset residual emissions to reach the net-zero target [3]. The role of NETs in addressing hard-to-abate emissions was further highlighted in the third installment of the AR6 report, “Mitigation of Climate Change” [1].

NETs can be grouped according to their carbon capture mechanism (biological, geochemical, or chemical) [1]. Many biological pathways are also classified under the term “natural climate solutions” [26,27]. The most prevalent NETs in the literature, AR, BECCS, wetland restoration (WR), soil carbon sequestration (SCS), biochar (BC), ocean fertilization (OF), enhanced weathering (EW), ocean alkalinization (OA), and direct air carbon capture and storage (DACCS) are summarized in Table 1. Industrial systems can also be designed to become carbon-negative using the underlying principles of the NETs in Table 1.

As with any large-scale technology, NETs are not without risks. They have biogeochemical and technological limits and may cause unintended side effects [24]. The possible negative consequences of BECCS and AR were reiterated in AR6, as the maladaptation of BECCS and AR may result in risks such as damage to biodiversity, water and food security, and livelihoods [28]. NETs also have uncertain performance and costs due to immaturity [29]. NETs strategies should be tailored to fit national or regional targets and conditions [1]. Aside from resource constraints, NETs are also bound to temporal constraints such as technological readiness and time-varying peak CDR potentials [17,30]. Inherent uncertainties in the scale and permanence of CDR should also be considered [31]. These aspects call for careful planning of NETs deployment [32]. NETs portfolios optimized for local conditions can be more sustainable [17]. Fig 2 illustrates the range of available NETs and the resources potentially needed to achieve CO2 sequestration. Portfolio optimization models can be used to prescribe the mix of NETs to maximize CDR given economic and environmental constraints [12].

Fig 2. Superstructure for the optimization of NET portfolios.

Alternative NETs rely on different physical, chemical, or biological mechanisms to sequester carbon. Each option will incur a characteristic cost and environmental footprint profile per unit of negative emissions. Portfolio optimization models can be used to prescribe the mix of NETs to maximize negative emissions given the economic and environmental constraints. NET, negative emissions technology.

Optimization of NETs deployment

Mathematical programming models

MP models consist of an objective function subject to constraints in the form of equations, inequalities, and variable specifications [49]. They can handle complex, large-scale problems when properly formulated and structured. IAMs, which are based on the MP framework, optimize or simulate the global energy and other GHG-emitting systems [11]. Most IAM studies focus on BECCS due to its flexibility in transitioning energy systems to net-zero emissions [25]. It is also common to run IAMs as standalone energy systems to determine the optimum energy mix [11]. For example, a study investigated the impact of BECCS on the global energy mix using a linear programming (LP) optimization model [50]. Similar studies have been done for specific countries such as Japan [51] and the Netherlands [52]. Another study used an energy systems model to minimize the overall system cost while modeling complex carbon flows with fossil energy and biomass-based CCS/CCUS [53].

Most IAMs only focus on BECCS and AR applied individually [54]. Only 1 study to date simulated a NETs portfolio composed of BECCS, DACCS, AR, and EW, and concluded that a balanced portfolio is best from a regional perspective [55]. A review paper argues that IAMs should be supplemented by other models and analytical approaches to consider social and technical perspectives as well as local energy system conditions [11].

In PSE literature, models and algorithms are developed to find the optimum system configuration [56]. For instance, value chain optimization that considers the energy, water, food, and carbon nexus has been reported in NETs literature. Multiple conflicting objectives occur in such models, necessitating analysis of the Pareto front [57]. An example of a multi-objective NETs value chain optimization MP is the Modeling and Optimization of NET (MONET) [58]. The framework was applied to BECCS to generate the optimal supply chain by minimizing land and water use and maximizing CDR and electricity generation [58]. The framework was also demonstrated in a global BECCS network [59] and for national scenarios in the United Kingdom [60] and Qatar [61]. Another approach is life cycle optimization, which combines life cycle impact analysis and MP. This approach was demonstrated for the multi-objective optimization of a BECCS supply chain [62].

PI models are also used in optimizing the supply chains of NETs other than BECCS. In contrast with IAMs, these models are prescriptive rather than descriptive. The first paper on the optimal planning of negative emissions BC networks used a mixed-integer linear programming (MILP) model to maximize CDR by matching sources and sinks [63]. A subsequent study extended this model by adding an economic objective function and by considering the quality of the receiving soils as a sink [64]. BC systems were further explored by generating near-optimal solutions using integer cuts [65] since these solutions may exhibit desirable properties not reflected in MPs [66]. A hybrid renewable energy system with BC production was optimized in another bi-objective model that maximizes both cost and CDR [67]. The BC network was further improved by considering higher resolution constraints (CDR, and land, water, and nutrient footprints), costs, and transport network topology [68]. MP models have also been applied to EW networks. An LP model for maximizing EW CDR by matching rock-crushing plants with rock application sites under temporal constraints was developed [69]. Models have also been proposed for the optimal design of EW networks using alkaline industrial waste in Taiwan [70] and mainland China [71].

MPs have also been proposed for integrating NETs into existing systems. For example, indirect biomass co-firing was modeled in existing coal-fired power plants wherein BC is co-produced and applied to the soil for negative emissions [72]. Another study included negative emissions BC production in optimizing combined heat and power systems with renewable (biomass and solar) energy sources [73]. A study optimized networks of ethanol biorefineries and CCS plants in the United States for total cost using integer programming [74]. Polygeneration systems, which deliver combined cooling, heating, and power, can be integrated with a negative emissions desalination process based on OA [75] while considering hourly variations in product demand and electricity price [76].

There are few PI models in the literature that consider NET portfolios. An MP model integrated BC and EW in the same system and capitalized on the synergistic relationship between the 2 NETs [77]. Another LP model optimized a NETs portfolio with AR, SCS, BECCS, BC, EW, and DACCS by evaluating the environmental footprints of each technology [78]. This approach gives the optimum NETs mix by minimizing the total cost using the Planetary Boundaries as constraints [79]. Accounting for nutrient flows in biological NETs is also critical since phosphorous is a non-renewable resource; also excess fertilizer use causes eutrophication, while excess nitrogen generates nitrous oxide (N2O), a potent GHG [79]. The study was extended to an MILP model by considering the synergistic resource interactions between NETs [80].

MP is used in energy system models and value chain optimization commonly involving BECCS and is utilized in PI models involving BECCS, BC, EW, and rarely OA. The models usually consider cost, energy, carbon, land, and water footprints but rarely nutrient footprints. Multi-footprint models are also rare. Since NETs are commonly modeled individually, there have been few attempts to evaluate their synergistic or antagonistic interactions. Although MP is useful for detailed modeling, they have the drawback of poor interactivity when used for decision support. In the next section, alternative interactive methods will be discussed.

Graphical and algebraic pinch analysis extensions

In MP models, the optimization procedure is inherently detached from the thought processes of the decision-maker [81]. In contrast, interactive methods provide critical insights and visualization that are important in the first steps of decision analysis [82]. For example, CEPA has been extended to the planning of CCS systems [83] and more recently to NETs deployment planning (see Fig 3A; [84]). Derivative graphical [84,85] and algebraic techniques [86] have been proposed for the optimum deployment of NETs. A CEPA study of the UK’s decarbonization concluded that net-zero emissions can be achieved with BECCS [87]. CEPA has also been applied for planning individual NETs such as BC [88] and EW networks [89]. Graphical PA techniques can also be replaced with mathematically equivalent algebraic procedures for easier implementation using spreadsheets [56].

Fig 3. Graphical approaches in the optimization and prioritization of NETs deployment.

(A) CEPA adapted from [84] and (B) extended MAC curve adapted from [95]. CEPA, carbon emissions pinch analysis; MAC, marginal abatement cost; NET, negative emissions technology.

Another graphical technique, known as the marginal abatement cost (MAC) curve, was originally developed in the 1980s [90]. MAC curves were popularized by McKinsey & Company to identify global cost-effective GHG emissions reduction solutions [91]. Model-based MAC curves that run alongside IAMs have also been used in NETs analysis; MAC curves for reforestation and avoided deforestation were generated using economic models [92]. Another study examined the effect of BECCS on the overall system cost and MACs using energy and economic system models [93]. MAC curves were used with an energy model to select cost-efficient emissions reduction measures including biomass co-firing and CCS in Indonesia [94]. Expert-based MACs have been supplemented with algebraic targeting methods and other improvements. A minimum MAC approach, which is a hybrid graphical and algebraic targeting technique, was developed and has been illustrated in decarbonization planning as shown in Fig 3B [95].

The graphical approaches have focused on optimizing cost, energy, and carbon footprints. Demonstrating these tools on other footprints such as land, water, and nutrients can provide key insights on NETs deployment, but this remains as a research gap in the literature. Both MP and graphical approaches are powerful tools for NETs decision support. The next section discusses multi-criteria decision analysis (MCDA) techniques for ranking NETs.

Multi-criteria decision analysis techniques

In addition to quantifiable techno-economic criteria, it is also important to consider other intangible aspects of NETs, such as social acceptance, feasibility, secondary impacts, and co-benefits [96]. MCDA techniques are useful tools for the selection and prioritization of NETs considering these indicators. A study combined 2 popular MCDA methods, the analytic hierarchy process (AHP) [97] and the technique for order preference by similarity to ideal solution (TOPSIS) [98] to rank NETs based on technical readiness, potential capacity, cost, and energy requirement [99]. Another study used MCDA to evaluate various NETs based on feasibility, effectiveness, and side impacts [30]. A neutrosophic data envelopment analysis (NDEA) model was developed to rank NETs based on environmental footprints, costs, and the albedo effect [100]. A graphical decision-making tool based on a “2 × 2” framework to assess the regional value of NETs was proposed [101]. This approach maps options using “zero-carbon availability” and “benefit-cost” as the horizontal and vertical axes [101].

MCDA techniques provide decision support for ranking NETs based on hard and soft criteria. Miscellaneous decision support tools for NETs deployment will also be discussed in the next section.

Miscellaneous techniques

Machine learning (ML), which is a subset of AI, can handle tasks such as prediction, forecasting, or classification. ML techniques, which include random forests, support vector machines, and artificial neural networks, have been recently applied in NETs. Random forests were used to predict the yields of BC from slow pyrolysis, and then, the results were further subjected to LCA and economic analysis [102]. The same ML approach was used to predict the product yields and characteristics using hydrothermal treatment of different biomass feedstocks coupled with CCS [103]. A decision framework was developed to evaluate the economic feasibility of BECCS through a combination of ML, LCA, and economic analysis and was demonstrated in a regional case study [104].

Rule-based ML approaches have also been used for the classification of geological CO2 storage sites for BECCS or DACCS. These techniques that generate if-then rules have the advantage of inherent interpretability [105]. A study developed a rough set-based ML technique [106] to predict reliable CO2 storage sites. The rule-based classifier was trained on a dataset of CO2 storage sites with known geological attributes [107]. A rule-based hyperbox classifier trained using an MILP model has also been developed to identify secure CO2 storage sites [105].

The process graph (P-graph) framework was originally developed to solve process network synthesis (PNS) problems [108] and is now used to solve a range of analogous problems [109]. It has the advantage of being able to exhaustively generate optimal and near-optimal solutions, which is important in decision-making since the optimal solutions are sometimes impractical and less robust than the suboptimal solutions [66]. P-graph models have been proposed for planning NETs systems based on BC application [110] and OA [111]. An inductive MCDA technique based on P-graph has been proposed to rank NETs [112]. The approach is similar to ML in that it relies on learning from examples. Implementation of NETs considering government–industry interactions was modeled as a Stackelberg game and solved with P-graph [113]. P-graph was also used to generate causality maps to show complex influences between NETs barriers and enablers [114].

The various modeling approaches involving NETs presented in this work and the techniques matched with the tasks are summarized in Table 2. Note each has its own applicability, strengths, and limitations.

Table 2. NETs modeling techniques and the tasks involved.

Uncertainty analysis

Epistemic uncertainties due to a lack of knowledge of the system are common in emerging technologies like NETs [9]. Examples of epistemic uncertainties in NETs planning include uncertainties in performance and cost [17,29], resource constraints [115], permanence [16,116], and social acceptability [2,117]. Stochastic uncertainties, which arise from the randomness of the system, are also present in NET planning in the form of variations in the parameters [117]. Fuzzy, stochastic, and robust programming techniques can be used to deal with these uncertainties [118].

Fuzzy decision-making seeks the “confluence” of the fuzzy goals with the fuzzy constraints in a given problem [119]. Fuzzy MP can address epistemic uncertainties of model parameters [120] and perform non-compensatory, multi-objective optimization [77]. It has been widely applied in sustainable energy technologies [121]. Fuzzy optimization has been demonstrated on NETs such as BC from biomass co-firing [72], EW networks [70], integrated EW and BC networks [77], and multi-footprint optimization of NET portfolios [80].

Post-optimization sensitivity analysis can also be done to evaluate the parametric uncertainties in NETs. For example, this approach was applied to negative emission desalination by varying the price of treated brine [76] and to BC by varying CDR targets [68]. Optimized NETs portfolios were also subjected to sensitivity analysis by evaluating the parametric uncertainties in the resource constraints [78]. Monte Carlo simulation has been used for the analysis of suboptimal solutions generated using integer cuts [66] or P-graph [109]. This two-step approach has also been reported in the literature to identify robust emissions reduction strategies [29] and robust CCS networks [122].

Conclusions and future research outlook

Planning for NETs deployment will play a significant role in achieving global climate goals [12]. Although the climate change mitigation benefits of NETs deployment are global, the costs, risks, and environmental impacts (or co-benefits) may be geographically localized. Various modeling approaches have been surveyed in this work. Ubiquitous IAM models need to be supplemented with other computing tools capable of prescribing optimal decisions [11]. PI techniques can bridge this gap by reducing resource consumption, carbon footprint, and waste [4] in the implementation of NETs. The following specific research gaps and opportunities are identified:

  • Whereas IAMs have focused on BECCS and AR [54], there is a research gap in models involving other NETs. PI techniques can supplement IAMs by modeling other NETs such as BC, EW, and OA. PI tools can also be applied to carbon management networks and industrial processes involving NETs.
  • There are very few studies on NETs portfolios on smaller scales, even if their sustainability benefits are clear [16]. PI tools can support the optimization of NETs portfolios on regional scales to ease the impact of individual technologies and hedge risks of underdevelopment of individual technologies.
  • Multi-objective studies should consider cost, CDR, and environmental footprints [57]. Nutrients (nitrogen and phosphorous) footprints of biomass-based NETs need to be examined more closely relative to the Planetary Boundaries [79].
  • Graphical approaches like PA and MAC have been extended to NETs considering cost and CDR. These tools need to be improved to be able to handle other aspects of NETs deployment.
  • Multi-period models are available in the PI NETs literature, but they only consider short timeframes [76]. Future NETs planning models need to account for readiness and peak CDR potentials over multiple decades [96].
  • There is also a need to address the intangible aspects of NETs such as social acceptance, feasibility, secondary impacts, and co-benefits [30]. These aspects can be addressed by MCDA techniques. Models evaluating the synergistic and antagonistic effects between NETs are also needed. Portfolio optimization models can be used to determine the best mix of NETs considering these details.
  • The data-driven approach in NETs planning is an emerging research area that is foreseen to grow in the future [9]. P-graph goes beyond supply chain synthesis by performing other tasks such as determining criteria weights using training data (similar to ML), generating optimal and suboptimal solutions for game theory, and producing causality maps. The use of P-graphs for NETs remains limited and presents an opportunity for further research.
  • Lastly, planning NETs requires dealing with uncertainty [29]. Stochastic and robust MP models can be explored in future research.

The different techniques discussed in this work can be applied to the various tasks required in NETs planning to prescribe normative courses of action, rather than merely describing the results of predefined scenarios. These techniques can also mitigate the potential negative side impacts of NETs [17]. Planners need to carefully consider the local conditions before deploying NETs [1]. The optimization and decision support models presented in this work demonstrate the value of high-level decision support for developing climate policy.


This work was supported by a grant to MS and DCYF from The British Council Japan under the COP26 Trilateral Research Initiative and PNSBN received a salary from the grant. MVMS received additional support from the Department of Science and Technology of the Philippines via the ERDT PhD scholarship program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


  1. 1. IPCC. Summary for Policymakers. In: Shukla PR, Skea J, Slade R, Al Khourdajie A, van Diemen R, McCollum D, et al., editors. Climate Change 2022: Mitigation of Climate Change Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY, USA: Cambridge University Press; 2022.
  2. 2. The Royal Society. Greenhouse Gas Removal. UK: R Soc. London; 2018. Available from:
  3. 3. IPCC. Summary for Policymakers. In: Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, et al., editors. Climate Change 2021: The Physical Science Basis Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY, USA: Cambridge University Press; 2021.
  4. 4. El-Halwagi MM, Foo DCY. Process synthesis and integration. Kirk-Othmer Encyclopedia of Chemical Technology. New Jersey: Wiley; 2014. p. 1–24.
  5. 5. Klemeš JJ. Process Integration (PI): Introduction An. 2nd ed. In: Klemeš JJ, editor. Handbook of Process Integration (PI): Minimisation of Energy and Water Use, Waste and Emissions. 2nd ed. Cambridge, UK: Woodhead Publishing; 2022. p. 3–27.
  6. 6. Linnhoff B, Townsend DW, Boland D, Hewitt GF, Thomas BEA, Guy AR, et al. A User Guide on Process Integration for the Efficient Use of Energy. Rugby, UK: Institute of Chemical Engineers; 1982.
  7. 7. El-Halwagi MM, Manousiouthakis V. Synthesis of mass exchange networks. AICHE J. 1989;35:1233–1244.
  8. 8. Tan RR, Foo DCY. Pinch analysis approach to carbon-constrained energy sector planning. Energy. 2007;32:1422–1429.
  9. 9. Kong KGH, How BS, Teng SY, Leong WD, Foo DC, Tan RR, et al. Towards data-driven process integration for renewable energy planning. Curr Opin Chem Eng. 2021;31:100665.
  10. 10. Andiappan V, Foo DCY, Tan RR. Process-to-Policy (P2Pol): using carbon emission pinch analysis (CEPA) tools for policy-making in the energy sector. Clean Techn Environ Policy. 2019;21:1383–1388.
  11. 11. Gambhir A, Butnar I, Li P-H, Smith P, Strachan N. A Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address These, through the Lens of BECCS. Energies. 2019;12:1747.
  12. 12. Tan RR, Aviso KB, Foo DCY, Migo-sumagang MV, Nair P, Nair SB, et al. Computing optimal carbon dioxide removal portfolios. Nat Comput Sci. 2022;2:465–466.
  13. 13. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res. 2021;133:285–296.
  14. 14. McLaren D. A comparative global assessment of potential negative emissions technologies. Process Saf Environ Prot. 2012;90:489–500.
  15. 15. Smith P, Davis SJ, Creutzig F, Fuss S, Minx J, Gabrielle B, et al. Biophysical and economic limits to negative CO2 emissions. Nat Clim Chang. 2016;6:42–50.
  16. 16. Minx JC, Lamb WF, Callaghan MW, Fuss S, Hilaire J, Creutzig F, et al. Negative emissions—Part 1: Research landscape and synthesis. Environ Res Lett. 2018;13:063001.
  17. 17. Fuss S, Lamb WF, Callaghan MW, Hilaire J, Creutzig F, Amann T, et al. Negative emissions—Part 2: Costs, potentials and side effects. Environ Res Lett. 2018;13:063002.
  18. 18. Nemet GF, Callaghan MW, Creutzig F, Fuss S, Hartmann J, Hilaire J, et al. Negative emissions—Part 3: Innovation and upscaling. Environ Res Lett. 2018;13:063003.
  19. 19. Smith P, Adams J, Beerling DJ, Beringer T, Calvin KV, Fuss S, et al. Land-Management Options for Greenhouse Gas Removal and Their Impacts on Ecosystem Services and the Sustainable Development Goals. Annu Rev Environ Resour. 2019;44:255–286.
  20. 20. Terlouw T, Bauer C, Rosa L, Mazzotti M. Life cycle assessment of carbon dioxide removal technologies: a critical review. Energy Environ Sci. 2021;14:1701–1721.
  21. 21. Dyson FJ. Can we control the carbon dioxide in the atmosphere? Energy. 1977;2:287–291.
  22. 22. Xu M, Crittenden JC, Chen Y, Thomas VM, Noonan DS, Desroches R, et al. Gigaton problems need gigaton solutions. Environ Sci Technol. 2010;44:4037–4041. pmid:20462269
  23. 23. IPCC. Synthesis Report. In: Pachauri R., Reisinger A, editors. Climate Change 2007: Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC; 2007. 10.1136/bmj.39420.654583.25
  24. 24. IPCC. Synthesis Report. In: Pachauri RK, Meyer LA, editors. Climate Change 2014: Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland; 2014. p. 31.
  25. 25. Köberle AC. The Value of BECCS in IAMs: a Review. Curr Sustain Energy Reports. 2019;6:107–115.
  26. 26. Griscom BW, Busch J, Cook-Patton SC, Ellis PW, Funk J, Leavitt SM, et al. National mitigation potential from natural climate solutions in the tropics. Philos Trans R Soc B Biol Sci. 2020:375. pmid:31983330
  27. 27. Griscom BW, Adams J, Ellis PW, Houghton RA, Lomax G, Miteva DA, et al. Natural climate solutions. Proc Natl Acad Sci U S A. 2017;114:11645–11650. pmid:29078344
  28. 28. IPCC. Summary for Policymakers. In: Pörtner HO, Roberts DC, Tignor M, Poloczanska ES, Mintenbeck K, Alegría A, et al., editors. Climate Change 2022: Impacts, Adaptation, and Vulnerability Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, NY, USA: Cambridge University Press. In Press.; 2022.
  29. 29. Aviso KB, Ngo JPS, Sy CL, Tan RR. Target-oriented robust optimization of emissions reduction measures with uncertain cost and performance. Clean Techn Environ Policy. 2019;21:201–212.
  30. 30. Rueda O, Mogollón JM, Tukker A, Scherer L. Negative-emissions technology portfolios to meet the 1.5°C target. Glob Environ Chang. 2021;67:102238.
  31. 31. Arcusa S, Sprenkle-Hyppolite S. Snapshot of the Carbon Dioxide Removal certification and standards ecosystem (2021–2022). Clim Policy. 2022:1–14.
  32. 32. Iyer G, Clarke L, Edmonds J, Fawcett A, Fuhrman J, McJeon H, et al. The role of carbon dioxide removal in net-zero emissions pledges. Energy Clim Chang. 2021;2:100043. pmid:36204673
  33. 33. Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, et al. A large and persistent carbon sink in the world’s forests. Science (80-). 2011;333:988–993. pmid:21764754
  34. 34. Kraxner F, Schepaschenko D, Fuss S, Lunnan A, Kindermann G, Aoki K, et al. Mapping certified forests for sustainable management—A global tool for information improvement through participatory and collaborative mapping. For Policy Econ. 2017;83:10–18.
  35. 35. Smith P, Haszeldine RS, Smith SM. Preliminary assessment of the potential for, and limitations to, terrestrial negative emission technologies in the UK. Environ Sci Process Impacts. 2016;18:1400–1405. pmid:27731875
  36. 36. Zedler JB, Kercher S. Wetland resources: Status, trends, ecosystem services, and restorability. Annu Rev Environ Resour. 2005;30:39–74.
  37. 37. Smith P. Soil carbon sequestration and biochar as negative emission technologies. Glob Chang Biol. 2016;22:1315–1324. pmid:26732128
  38. 38. Sykes AJ, Macleod M, Eory V, Rees RM, Payen F, Myrgiotis V, et al. Characterising the biophysical, economic and social impacts of soil carbon sequestration as a greenhouse gas removal technology. Glob Chang Biol. 2020;26:1085–1108. pmid:31532049
  39. 39. Tisserant A, Cherubini F. Potentials, Limitations, Co-Benefits, and Trade-Offs of Biochar Applications to Soils for Climate Change Mitigation. Landscape. 2019;8:179.
  40. 40. Creutzig F, Ravindranath NH, Berndes G, Bolwig S, Bright R, Cherubini F, et al. Bioenergy and climate change mitigation: An assessment. GCB Bioenergy. John Wiley & Sons, Ltd; 2015. p. 916–944.
  41. 41. Creutzig F, Breyer C, Hilaire J, Minx J, Peters GP, Socolow R. The mutual dependence of negative emission technologies and energy systems. Energy Environ Sci. 2019;12:1805–1817.
  42. 42. Lampitt RS, Achterberg EP, Anderson TR, Hughes JA, Iglesias-Rodriguez MD, Kelly-Gerreyn BA, et al. Ocean fertilization: A potential means of geoengineering? Philos Trans R Soc A Math Phys Eng Sci. 2008. pmid:18757282
  43. 43. Renforth P. The potential of enhanced weathering in the UK. Int J Greenh Gas Control. 2012;10:229–243.
  44. 44. Beerling DJ, Leake JR, Long SP, Scholes JD, Ton J, Nelson PN, et al. Farming with crops and rocks to address global climate, food and soil security. Nat Plants. 2018;4:138–147. pmid:29459727
  45. 45. Strefler J, Amann T, Bauer N, Kriegler E, Hartmann J. Potential and costs of carbon dioxide removal by enhanced weathering of rocks. Environ Res Lett. 2018;13:034010.
  46. 46. Renforth P, Henderson G. Assessing ocean alkalinity for carbon sequestration. Rev Geophys. 2017;55:636–674.
  47. 47. Gore S, Renforth P, Perkins R. The potential environmental response to increasing ocean alkalinity for negative emissions. Mitig Adapt Strateg Glob Chang. 2019;24:1191–1211.
  48. 48. Gambhir A, Tavoni M. Direct Air Carbon Capture and Sequestration: How It Works and How It Could Contribute to Climate-Change Mitigation. One Earth Cell Press. 2019:405–409.
  49. 49. Biegler LT, Grossmann IE. Retrospective on optimization. Comput Chem Eng. 2004;28:1169–1192.
  50. 50. Selosse S, Ricci O. Achieving negative emissions with BECCS (bioenergy with carbon capture and storage) in the power sector: New insights from the TIAM-FR (TIMES Integrated Assessment Model France) model. Energy. 2014;76:967–975.
  51. 51. Kato E, Kurosawa A. Evaluation of Japanese energy system toward 2050 with TIMES-Japan—Deep decarbonization pathways. Energy Procedia Elsevier. 2019:4141–4146.
  52. 52. Sánchez Diéguez M, Fattahi A, Sijm J, Morales España G, Faaij A. Modelling of decarbonisation transition in national integrated energy system with hourly operational resolution. Adv Appl Energy. 2021;3:100043.
  53. 53. Li X, Damartzis T, Stadler Z, Moret S, Meier B, Friedl M, et al. Decarbonization in Complex Energy Systems: A Study on the Feasibility of Carbon Neutrality for Switzerland in 2050. Front Energy Res. 2020;8:549615.
  54. 54. Rickels W, Merk C, Reith F, Keller DP, Oschlies A. (Mis)conceptions about modeling of negative emissions technologies. Environ Res Lett. 2019;14:104004.
  55. 55. Strefler J, Bauer N, Humpenöder F, Klein D, Popp A, Kriegler E. Carbon dioxide removal technologies are not born equal. Environ Res Lett. 2021;16:074021.
  56. 56. Stephanopoulos G, Reklaitis GV. Process systems engineering: From Solvay to modern bio- and nanotechnology. A history of development, successes and prospects for the future. Chem Eng Sci. 2011;66:4272–4306.
  57. 57. Haimes YY, Hall WA. Multiobjectives in water resource systems analysis: The Surrogate Worth Trade Off Method. Water Resour Res. 1974;10:615–624.
  58. 58. Fajardy M, Mac Dowell N. Can BECCS deliver sustainable and resource efficient negative emissions? Energy Environ Sci. 2017;10:1389–1426.
  59. 59. Fajardy M, Chiquier S, Mac Dowell N. Investigating the BECCS resource nexus: delivering sustainable negative emissions. Energy Environ Sci. 2018;11:3408–3430.
  60. 60. Bui M, Zhang D, Fajardy M, Mac Dowell N. Delivering carbon negative electricity, heat and hydrogen with BECCS–Comparing the options. Int J Hydrog Energy. 2021;46:15298–15321.
  61. 61. Namany S, Al-Ansari T, Govindan R. Optimisation of the energy, water, and food nexus for food security scenarios. Comput Chem Eng. 2019;129:106513.
  62. 62. Negri V, Galán-Martín Á, Pozo C, Fajardy M, Reiner DM, Mac Dowell N, et al. Life cycle optimization of BECCS supply chains in the European Union. Appl Energy. 2021;298:117252.
  63. 63. Tan RR. A multi-period source–sink mixed integer linear programming model for biochar-based carbon sequestration systems. Sustain Prod Consum. 2016;8:57–63.
  64. 64. Belmonte BA, Francis M, Benjamin D, Tan RR. Bi-objective optimization of biochar-based carbon management networks. 2018.
  65. 65. Belmonte BA, Francis M, Benjamin D, Tan RR. Optimization-based decision support methodology for the synthesis of negative-emissions biochar systems. Sustain Prod Consum. 2019;19:105–116.
  66. 66. Voll P, Jennings M, Hennen M, Shah N, Bardow A. The optimum is not enough: A near-optimal solution paradigm for energy systems synthesis. Energy. 2015;82:446–456.
  67. 67. Li L, You S, Wang X. Optimal design of standalone hybrid renewable energy systems with biochar production in remote rural areas: A case study. Energy Procedia Elsevier; 2019. p. 688–693.
  68. 68. Ong SH, Tan RR, Andiappan V. Optimisation of biochar-based supply chains for negative emissions and resource savings in carbon management networks. Clean Techn Environ Policy. 2021;23:621–638.
  69. 69. Tan RR, Aviso KB. A linear program for optimizing enhanced weathering networks. Res Eng Des. 2019;3:100028.
  70. 70. Aviso KB, Lee JY, Ubando AT, Tan RR. Fuzzy optimization model for enhanced weathering networks using industrial waste. Clean Techn Environ Policy. 2022;24:21–37.
  71. 71. Jia X, Zhang Z, Wang F, Li Z, Wang Y, Aviso KB, et al. Regional carbon drawdown with enhanced weathering of non-hazardous industrial wastes. Resour Conserv Recycl. 2022;176:105910.
  72. 72. Aviso KB, Sy CL, Tan RR, Ubando AT. Fuzzy optimization of carbon management networks based on direct and indirect biomass co-firing. Renew Sust Energ Rev. 2020;132:110035.
  73. 73. Bowley W, Evins R. Energy system optimization including carbon-negative technologies for a high-density mixed-use development. Int J Sustain Energy Plan Manag. 2021;31:211–225.
  74. 74. Sanchez DL, Johnson N, McCoy ST, Turner PA, Mach KJ. Near-term deployment of carbon capture and sequestration from biorefineries in the United States. Proc Natl Acad Sci U S A. 2018;115:4875–4880. pmid:29686063
  75. 75. Davies PA, Yuan Q, De Richter R. Desalination as a negative emissions technology. Environ Sci Water Res Technol. 2018;4:839–850.
  76. 76. Tan RR, Aviso KB, Foo DCY, Lee JY, Ubando AT. Optimal synthesis of negative emissions polygeneration systems with desalination. Energy. 2019;187:115953.
  77. 77. Belmonte BA, Aviso KB, Benjamin MFD, Tan RR. A fuzzy optimization model for planning integrated terrestrial carbon management networks. Clean Techn Environ Policy. 2022;24:289–301.
  78. 78. Migo-Sumagang MV, Aviso K, Tapia JF, Tan RR. A Superstructure Model for Integrated Deployment of Negative Emissions Technologies under Resource Constraints. Chem Eng Trans. 2021;88:31–36.
  79. 79. Rockström J, Steffen W, Noone K, Persson Å Chapin FS III, Lambin EF, et al. A safe operating space for humanity. Nature. 2009;461:472–475. pmid:19779433
  80. 80. Migo-Sumagang MV, Tan RR, Tapia JFD, Aviso KB. Fuzzy mixed-integer linear and quadratic programming models for planning negative emissions technologies portfolios with synergistic interactions. Clean Eng Technol. 2022;9:100507.
  81. 81. Smith R. Chemical Process Design and Integration. 2nd ed. John Wiley & Sons, Ltd. Wiley; 2005.
  82. 82. Klemeš JJ, Varbanov PS, Kravanja Z. Recent developments in Process Integration. Chem Eng Res Des. 2013;91:2037–2053.
  83. 83. Ooi REH, Foo DCY, Ng DKS, Tan RR. Planning of carbon capture and storage with pinch analysis techniques. Chem Eng Res Des. 2013;91:2721–2731.
  84. 84. Nair PNSB, Tan RR, Foo DCY. Extended graphical approach for the implementation of energy-consuming negative emission technologies. Renew Sust Energ Rev. 2022;158:112082.
  85. 85. Nair PNSB Tan RR, Foo DCY. Extended Graphical Approach for the Deployment of Negative Emission Technologies. Ind Eng Chem Res. 2020;59:18977–18990.
  86. 86. Nair PNSB Tan RR, Foo DCY. A generic algebraic targeting approach for integration of renewable energy sources, CO2 capture and storage and negative emission technologies in carbon-constrained energy planning. Energy. 2021;235:121280.
  87. 87. Cossutta M, Foo DCY, Tan RR. Carbon emission spinch analysis (CEPA) for planning the decarbonization of the UK power sector. Sustain Prod Consum. 2021;25:259–270.
  88. 88. Tan RR, Bandyopadhyay S, Foo DCY. Graphical Pinch Analysis for Planning Biochar-Based Carbon Management Networks. Process Integr Optim Sustain. 2018;2:159–168.
  89. 89. Tan RR, Aviso KB, Bandyopadhyay S. Pinch-based planning of terrestrial carbon management networks. Clean Eng Technol. 2021;4:100141.
  90. 90. Meier A, Rosenfeld AH, Wright J. Supply curves of conserved energy for California’s residential sector. Energy. 1982;7:347–358.
  91. 91. Enkvist P-A, Nauclér T, Rosander J. A cost curve for greenhouse gas reduction. In: Mckinsey [Internet]. 1 Feb 2007 [cited 2022 Mar 21]. p. 1–34. Available from:
  92. 92. Busch J, Engelmann J, Cook-Patton SC, Griscom BW, Kroeger T, Possingham H, et al. Potential for low-cost carbon dioxide removal through tropical reforestation. Nat Clim Chang. 2019;9:463–466.
  93. 93. Tatarewicz I, Lewarski M, Skwierz S, Krupin V, Jeszke R, Pyrka M, et al. The Role of BECCS in Achieving Climate Neutrality in the European Union. Energies. 2021;14:7842.
  94. 94. Nurfajrin ZD, Satiyawira B. Abatement cost for selectivity negative emissions technology in power plant Indonesia with aim/end-use model. IOP Conf Ser Earth Environ Sci. 2021;894:012011.
  95. 95. Lameh M, Al-Mohannadi DM, Linke P. Minimum marginal abatement cost curves (Mini-MAC) for CO2 emissions reduction planning. Clean Techn Environ Policy. 2022;24:143–159.
  96. 96. Fridahl M, Hansson A, Haikola S. Towards Indicators for a Negative Emissions Climate Stabilisation Index: Problems and Prospects. Climate. 2020;8:75.
  97. 97. Saaty TL. Conflict Resolution and the Falkland Islands Invasions. Interfaces (Providence, Rhode Island). 1983;13:68–83.
  98. 98. Yoon K, Hwang C-L. Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey. Berlin, Heidelberg: Springer; 2011.
  99. 99. Ng WY, Low CX, Putra ZA, Aviso KB, Promentilla MAB, Tan RR. Ranking negative emissions technologies under uncertainty. Heliyon. 2020;6:e05730. pmid:33364497
  100. 100. Tapia JFD. Evaluating negative emissions technologies using neutrosophic data envelopment analysis. J Clean Prod. 2021;286:125494.
  101. 101. Uden S, Dargusch P, Greig C. Cutting through the noise on negative emissions. Joule. 2021;5:1956–1970.
  102. 102. Cheng F, Luo H, Colosi LM. Slow pyrolysis as a platform for negative emissions technology: An integration of machine learning models, life cycle assessment, and economic analysis. Energy Convers Manag. 2020;223:113258.
  103. 103. Cheng F, Porter MD, Colosi LM. Is hydrothermal treatment coupled with carbon capture and storage an energy-producing negative emissions technology? Energy Convers Manag. 2020;203:112252.
  104. 104. Cheng F, Small AA, Colosi LM. The levelized cost of negative CO 2 emissions from thermochemical conversion of biomass coupled with carbon capture and storage. Energy Convers Manag. 2021;237:114115.
  105. 105. Tan RR, Aviso KB, Janairo JIB, Promentilla MAB. A hyperbox classifier model for identifying secure carbon dioxide reservoirs. J Clean Prod. 2020;272:122181.
  106. 106. Pawlak Z. Rough sets. Int J Comput Inform Sci. 1982;11:341–356.
  107. 107. Aviso KB, Janairo JIB, Promentilla MAB, Tan RR. Prediction of CO2 storage site integrity with rough set-based machine learning. Clean Techn Environ Policy. 2019;21:1655–1664.
  108. 108. Friedler F, Blicket T, Gyenis J, Tarjáns K. Computerized generation of technological structures. Comput Chem Eng. 1979;3:241–249.
  109. 109. Friedler F, Aviso KB, Bertok B, Foo DC, Tan RR. Prospects and challenges for chemical process synthesis with P-graph. Curr Opin Chem Eng. 2019;26:58–64.
  110. 110. Aviso KB, Belmonte BA, Benjamin MFD, Arogo JIA, Coronel ALO, Janairo CMJ, et al. Synthesis of optimal and near-optimal biochar-based Carbon Management Networks with P-graph. J Clean Prod. 2019;214:893–901.
  111. 111. Pimentel J, Orosz Á, Aviso KB, Tan RR, Friedler F. Conceptual design of a negative emissions polygeneration plant for multiperiod operations using P-graph. PRO. 2021;9:233.
  112. 112. Low CX, Ng WY, Putra ZA, Aviso KB, Promentilla MAB, Tan RR. Induction approach via P-Graph to rank clean technologies. Heliyon. 2020;6:e03083. pmid:31909259
  113. 113. Tan RR, Aviso KB, Walmsley T. P-graph Approach to Solving a Class of Stackelberg Games in Carbon Management. Chem Eng Trans. 2021;89:463–468.
  114. 114. Tan RR, Aviso KB, Lao AR, Promentilla MAB. P-graph Causality Maps. Process Integr Optim Sustain. 2021;5:319–334.
  115. 115. Steffen W, Richardson K, Rockström J, Cornell SE, Fetzer I, Bennett EM, et al. Planetary boundaries: Guiding human development on a changing planet. Science (80-). 2015;347:1259855.
  116. 116. Marland G, Fruit K, Sedjo R. Accounting for sequestered carbon: The question of permanence. Environ Sci Pol. 2001;4:259–268.
  117. 117. Tan RR, Aviso KB. On life-cycle sustainability optimization of enhanced weathering systems. J Clean Prod. 2021;289:125836 Contents.
  118. 118. Sahinidis NV. Optimization under uncertainty: State-of-the-art and opportunities. Comput Chem Eng. 2004;28:971–983.
  119. 119. Bellman RE, Zadeh LA. Decision-Making in a Fuzzy Environment. Manag Sci. 1970;17:B-141-B–164.
  120. 120. Carlsson C, Korhonen P. A parametric approach to fuzzy linear programming. Fuzzy Sets Syst. 1986;20:17–30.
  121. 121. Arriola ER, Ubando AT, Chen WH. A bibliometric review on the application of fuzzy optimization to sustainable energy technologies. Int J Energy Res. 2022;46:6–27.
  122. 122. Tan RR, Aviso KB, Foo DCY. P-graph and Monte Carlo simulation approach to planning carbon management networks. Comput Chem Eng. 2017;106:872–882.