Five years before Russia’s full-scale invasion of Ukraine, then-President of Ukraine Petro Poroshenko made a remarkable assertion. During a 2018 address commemorating the end of World War II, he congratulated Ukraine’s Ministry of Defence on Ukraine’s military becoming one of the ten most powerful in Europe (Kuzmenko 2018). While most listeners would not register this as unusual, military analysts and security scholars were likely intrigued by the claim: how could Poroshenko make this declaration with any confidence when the elements of military power are so extensive and varied as to defy authoritative comparison?
Investigative journalist Oleksiy Kuzmenko reveals that Poroshenko cited rankings from an opaque, commercial and self-styled entertainment site called
While the eagerness of a staff to inject some high notes into a leader’s remarks may be understandable, the episode raises a genuine issue: given the importance of military strength (however conceived) to the international distribution of power, the lack of accessible, rigorous methods for comparing military capabilities implies that journalists and government staff may continue citing commercial sources purporting to perform such analysis even if they lack credibility.
In this article, I propose a new contribution to the field of comparative analysis of state conventional military capabilities. First, I review perspectives of other scholars on the merits of comparing capabilities, arguing that the most accessible insights lie in the signals sent by state arsenals, rather than in predicting conflict outcomes, judging from state armament. Second, I present the Conventional Firepower Potential Indexing (CFPI) method and demonstrate that coding for tactical role and degree of technological sophistication enables previously unfeasible estimative comparisons of deterrent signalling value. Finally, I apply CFPI analysis to the conventional arsenals of the United States and the four states named in that country’s most recent National Defense Strategy (China, Russia, North Korea and Iran), deriving conclusions that would be more difficult without accessible comparative analysis.
In this section, I review selected perspectives on merits and challenges inherent in making comparisons between state capabilities. Noting that capability analysis – particularly arsenal analysis – alone is unreliable in predicting conflict outcomes, I posit that the prevalent use of major military hardware is to contribute to strategic signalling rather than to prosecute conflict. I then highlight extant methods for arsenal analysis and derive principles for a signalling value-focused approach.
While it seems intuitive to apply comparative arsenal analysis to conflict outcome prediction, compelling scholarship indicates materiel-focused analysis is unreliable. Carroll and Kenkel note that capability-based conflict outcome prediction performs only one percent better than a coin flip, while their own substantially improved method fares only 20% better (Carroll and Kenkel 2019). Biddle demonstrates convincingly that insight into conflict outcomes comes from states’ employment of their forces during combat, an approach that, to have predictive value, would require reliable estimates of how a state’s military
These lessons run into an empirical challenge: Most states do not use their arsenals for interstate conflict. Sarkees and Wayman’s exhaustive examination of interstate conflicts reveals that in the 60 years following World War II, fewer than 60 state governments – less than a third of the 188 accorded undisputed sovereign status by the United Nations – engaged in interstate armed conflict. In the preceding 60 years, over 120 distinct states engaged in such conflict over substantially longer durations (Sarkees and Wayman 2010). This observation may seem odd coming in the midst of a prominent interstate war sparked by Russia’s 2022 full-scale invasion of Ukraine; however, the very exceptional nature of this conflict highlights the continuing rarity of such occurrences compared to past periods (Feltman 2023). It remains true that the modern era sees most states purchasing and retaining conventional weapons that spend the vast majority – or entirety – of their existences unused in combat.
It is not clear that many states could employ their arsenals in any sustained way even if they were to commit to interstate conflict. An International Peace Institute survey of United Nations Peacekeeping Operations (UNPKO) suggests most states struggle to project and sustain even small fractions of their militaries over short distances for more than a few weeks (Coleman and Williams 2017). Nor is this challenge unique to the generally smaller and more developing pool that typically participates in UNPKO; a study by RAND concluded that the United Kingdom, France, and Germany—developed states with some prevalence of premier conventional armaments – would each be hard-pressed to marshal, deploy and sustain a single brigade of combat power
Given that the majority of state-owned military hardware never sees combat and that many states would struggle to employ their arsenals, widespread procurement of combat systems without addressing logistical deficiencies suggests a major aim of acquiring weaponry is merely having it. Scholars identify weapon possession as the capability dimension of conventional strategic signalling capacity, where credibility (reputational willingness to employ weapons for strategic aims) and communication (explicit statements from the state to others) constitute the other two dimensions (Gerson 2009; Morgan 2012; Haffa 2018). Although this bears deeper examination as an example, consider that as of January 2022, much of Europe prepared to negotiate on Russia’s Ukraine-focused demands owing to a combination of
The premise that conventional weapons contribute to a state’s strategic signalling capacity yields an avenue for comparative analysis. Where most weapons are never employed in conflict, all weapons (save those successfully concealed) contribute to signalling. The relative signalling contribution of a weapon is a less complicated phenomenon to estimate than its prospective combat use, an activity that entails innumerable factors. With this in mind, I survey selected methods of arsenal computation to derive lessons for signalling capacity estimation and identify precursor techniques for the CFPI method.
An impressive recent innovation in comparative arsenal analysis, the
A majority of extant analytic methods attempting quality distinctions between conventional weapons purport to project their performance under certain combat conditions. The archetype of these is the venerable Lanchester set of models, which – despite being re-validated by RAND as highly informative for engagement modelling – undercuts its feasibility by assuming large-scale engagements involving simultaneously firing masses of weapons (Lanchester 1916; Darilek et al. 2001). Innovations in this tradition-modifying Lanchester’s concepts for guided weapons and modern defences similarly attempt attritive results, rather than inherent comparative value for the systems themselves, attracting criticism for unwieldiness (Hughes 1995; Lucas and McGunnigle 2003; Armstrong 2013).
Three techniques that distinguish themselves from the Lanchester and related conflict outcome methods are (1) the summation technique in the United States Naval Postgraduate School’s aggregated firepower score (AFS) method; (2) Dubois et al.’s algebraic incorporation of combat power
The AFS method also attempts engagement outcome prediction, but approaches it distinctly from Lanchester and other attritive tools. While Lanchester and salvo models attempt to project casualties and survivors by matching weapon systems on each side of an engagement, the AFS method adopts the straightforward but elegant solution of coding values to different types of equipment, multiplying these by their quantity and then adding them to the scores of other systems to aggregate a score for all equipment arrayed in a given engagement (Naval Postgraduate School 2000). AFS is arguably far too reductive for predicting the outcome of an activity as complex as combat but provides an obvious precursor technique for a comparative method to estimate weapon systems’ inherent signalling value, rather than to predict their combat performance.
In their theory, Dubois, Hughes and Low express the potential firepower inherent in any weapon system as a component of a comprehensive model of combat power (Dubois et al. 1997). Isolating a facet of combat power that solely consists of the inherent potential firepower of a weapon system offers a proxy for signalling; the capability-based signalling value of a weapon logically resides in its
Finally, the WEI method piloted by the now-defunct U.S. Army Concepts Analysis Agency differentiated between degrees of technological sophistication among weapons of the same tactical role with weighted coefficients (U.S. Army Concepts Analysis Agency 1991). A major limitation of the WEI was the need for recurrent re-evaluation by panels of experts with divergent views on the indexed systems’ effectiveness in combat, one of the shortcomings that that Ben-Haim partly mitigates by adding robustness (Ben-Haim 2018). Avoiding the complex task of engagement outcome prediction by focusing on signalling value contribution means a weighted coefficient concept can be used without constant re-evaluation for effectiveness.
The next section of this paper incorporates WEI’s weighting concept, AFS’ role-sensitive summation approach and Dubois et al.’s expression of potential into processes to compute relative signalling capacity contribution by conventional systems.
This section describes the CFPI method’s computational processes. First, I algebraically derive the CFPI processes from the precursor techniques. Second, I illustrate the CFPI accounting for tactical roles and technological sophistication of weapons using a comparative example (China and Russia air-focused CFPI in 2023). Third, I note both constraints and possibilities of CFPI-in-formed analysis.
The CFPI method uses conventional firepower potential as a proxy for capability-based strategic signalling capacity. The following computational processes are intended only to abstractly score capability contributing to signalling. See
The firepower potential of a set of conventional weapons is the sum of the products of each system’s role, technological sophistication, and quantity. In this approach, the CFPI builds on the precursor techniques of AFS, DuBois et al.’s algebraic expression of combat power, and the WEI method’s weighted coefficient approach. These techniques are expressed as follows:
The AFS expression yields the total firepower assessed for weapons of type
Eliminating the aim of predicting combat effectiveness or engagement outcomes sidesteps the challenge of modelling ‘realising actions’ and means that only certain elements of these concepts apply to an index of strategic signalling value. Combining applicable concepts of the three methods means that the CFPI score – an approximation of the
This expresses the CFPI score for a group of one weapon type drawn from two generational tiers of sophistication
This expression uses as many terms as necessary to account for all types of weapon categorised as belonging to the domain. To make this concrete, the following expresses the CFPI score of a state’s major conventional weapons focused on the air domain:
The
The subscript
The preceding paragraphs algebraically express the process of indexing a state’s conventional arsenal into firepower potential scores. To enumerate these algebraic expressions, we must compute a value for the coefficient α. The next subsection details enumeration of α with proxy values for the tactical role and relative technological sophistication of each system in the CFPI.
The CFPI derives a value for each system type’s intended tactical role and a generational tier coefficient for technological sophistication. The overall coefficient applied to each system quantity is expressed as follows:
The subscript
To estimate
‘Raw’ firepower potential is the product of a system’s single-engagement explosive yield, index munition range, and operational range (or two-hour travel range in the case of naval vessels) with all ranges expressed in hundreds of kilometres. In every system’s case, this product is multiplied by a scaling constant of 0.036 and rounded to the nearest whole number solely to achieve a more intuitive scale across the CFPI. In the following expression – not reflecting these last two scaling steps –
For engagement explosive yield, the CFPI uses estimated energy yield in megacalorie (Mcal) TNT equivalence of the index munition’s explosive mass, assuming it behaves consistent with tritonal explosive’s properties (a mixture of 80% trinitrotoluene and 20% aluminium commonly employed in modern munitions and releasing approximately 18% more energy than a comparable mass of TNT) (U.S. Department of Energy 2002). This assumption uses the U.S. National Institute for Standards and Technology’s TNT equivalence convention of one gram of TNT releasing 4.184 kilojoules or one kilocalorie; one kilogram of tritonal explosive would yield approximately 1.18 megacalories (U.S. Department of Commerce 2008). This, in turn, is multiplied by aimed releases of the index munition by the index system in the space of a single minute.
The following steps compute the
Next, we multiply the engagement explosive yield by the index munition range and the index system range. Multiplying this product by the scaling coefficient of 0.036 and rounding provide the normalised yield ratio, the computed firepower potential precursor of tactical role value.
The last step in deriving role value for a weapon system type is to apply ordinal weight for release versatility and engagement versatility. Release versatility expresses the index system’s advertised adaptiveness to target behaviour when releasing the index munition, while engagement versatility accounts for two factors: (1) whether the index system is ordinarily intended to engage in one or multiple domains and (2) whether the index system is ordinarily configured to engage the systems designed to neutralise it. Versatility is multiplicative because it increases the reach and impact of the potential firepower inherent in an index munition as employed by an index system; in signalling terms, this may be interpreted as abstracting the theoretical spatial threat telegraphed by the platform in question. Understanding versatility as a degree-of-freedom-determined spatial threat may help the reader to appreciate the considerable signalling value that the CFPI accords to aircraft-carrying vessels. Table 1 offers a rubric for determining release and engagement versatility.
CFPI Release and Engagement Versatility Rubric
Value | Release Versatility | Engagement Versatility |
---|---|---|
3 | Index system releases systems of release versatility 2 that in turn release the index munition, giving the index system multiple levels of release articulation and adaptiveness to target behaviour | Index system is ordinarily intended to engage systems in multiple domains and is ordinarily configured to engage those systems purpose-built to target the index system |
2 | Index system can manoeuvre leading up to and during index munition release allowing a larger window of adaptation to target behaviour | Index system is ordinarily intended to engage systems in multiple domains or is ordinarily configured to engage those systems purpose-built to target the index system |
1 | Index system must be motionless to release the index munition; the index system cannot make dynamic adaptations to target behaviour immediately leading up to or upon release of the index munition | Index system is ordinarily intended to engage systems in only one domain and is not ordinarily configured to engage those systems purpose-built to target the index system |
Remembering that the CFPI’s tactical role value for a weapon system type is the product of normalised yield, release versatility, and engagement versatility, the tactical role value of multirole fighters thus computes.
Table 2 contains the weapon types, index systems, normalised yields, versatilities, and
Weapon System Role Values
Domain | Role | Index System | Index Munition | Normal Yield Ratio | Versatility | r-Value | |
---|---|---|---|---|---|---|---|
Release | Engagement | ||||||
Air | Air Superiority Fighter | F-16A (USA) | AIM-120 | 6 | 2 | 2 | 24 |
Multirole Fighter | F-16 C (Blk40+) (USA) | GBU-12 | 8 | 2 | 2 | 32 | |
Ground Attack Aircraft | A-IOC(USA) | GBU-12 | 12 | 2 | 1 | 24 | |
Air Defence (Missile) | MIM-104C (USA) | PAC-2 | 24 | 1 | 1 | 24 | |
Land | Main Battle Tank | MIA2SEP(USA) | M830A1 | 2 | 2 | 2 | 8 |
Armored Fighting Vehicle | ?2A3 (USA) | M792 | 1 | 2 | 2 | 4 | |
Self-Propelled Cannon Artillery | M109A6 (USA) | M483A1 DPICM | 2 | 1 | 1 | 2 | |
Towed Cannon Artillery | M119A1 (USA) | M915 DPICM | 1 | 1 | 1 | 1 | |
Rocket Artillery | M270A1 (USA) | M26A2 DPICM | 3 | 1 | 1 | 3 | |
Rotary Wing Attack | AH-64A (USA) | AGM-114N | 4 | 2 | 2 | 16 | |
Multirole Armed Rotary Wing | MH-6OA (USA) | 7.62 X 51mm NATO | 1 | 2 | 2 | 4 | |
Air Defence (Gun) | ZSU-23-4 (RUS) | 23xl52B BZT | 1 | 1 | 1 | 1 | |
Surface-to-Surface Missile | DF-I6(PRC) | DF-16 Conventional | 24 | 1 | 1 | 24 | |
Naval | Aircraft Carrier (Nuclear-powered) | Nimitz-Class (USA) | AGM-l54Cvia AV-8B | 320 | 3 | 3 | 2,880 |
Aircraft Carrier (Non-nuclear-powered) | America-Class (USA) | AGM-l54Cvia AV-8B | 108 | 3 | 3 | 972 | |
Helicopter Carrier | Canberra-Class (AUS) | AGM-114B via MH-6OR | 12 | 3 | 3 | 108 | |
Ship-Based Armed Rotary Wing | MH-6OS (USA) | AGM-114B | 3 | 2 | 1 | 6 | |
Ship-Based Armed Fixed Wing | AV-8B(USA) | AGM-154C | 8 | 2 | 2 | 32 | |
Ground-Based Armed Maritime Fixed Wing | P-8A (USA) | Mk-46 Mod 5 | 12 | 2 | 1 | ||
Cruiser | Ticonderoga-Class (USA) | RGM-109E | 75 | 2 | 3 | 450 | |
Destroyer | Arleigh Burke (fit. II) (USA) | RGM-109E | 50 | 2 | 3 | 300 | |
Frigate | Grigorovich (RUS) | P-800 | 35 | 2 | 2 | 140 | |
Corvette | Type O56A (PRC) | YJ-83K | 15 | 2 | 2 | 60 | |
Missile Boat/Fast AttackCraft | Type 022 (PRC) | YJ-83K | 12 | 2 | 1 | 24 | |
Tactical Submarine (Nuclear-powered) | Los Angeles-Class (fit. Ill) (USA) | RGM-109E | 50 | 2 | 3 | 300 | |
Tactical Submarine (Non-nuclear-powered) | Kilo-Class (Improved) (RUS) | 53-65M | 15 | 2 | 2 | 60 | |
Ground-Based Anti-Ship Missile | YJ-100(PRC) | YJ-100 | 30 | 1 | 1 | 30 |
Reference: U.S. Army Worldwide Equipment Guide.
To enumerate
WEG-CFPI Technological Tier Conversion and Coefficient Weighting
WEG Tier | CFPI Tier | Descriptor (2005-pres.) | Descriptor (1990-2004) | Adjustments (additions) | |
---|---|---|---|---|---|
1(+)* | 4 | Cutting-edge | N/A | 6 | System introduction establishes new generation; long-range missile systems of |
1 | 3 | Advanced | Cutting-edge | 4 | Long-range missile systems of |
2 | 2 | Competitive | Advanced | 3 | Long-range missile systems of |
3 | 1 | Ageing | Competitive | 1 | Long-range missile systems of |
4 | 1 | Ageing | Ageing | 1 | Short-range missile systems of |
4(-)* | 0 | Obsolete | Obsolete | 0 | Short-range missile systems of |
Reference: U.S. Army Worldwide Equipment Guide.
Denotes an equivalent tier that does not exist in the WEG labelled as such.
Like the
In this brief demonstration, the computational procedures from the previous section generate index scores for the conventional weapon systems of the People’s Republic of China and the Russian Federation in the air domain as of 2023. Beginning with the expression for overall CFPI score, I expand the expression for score within a single domain (air) and expand and compute CFPI score for a single system type (multirole fighters). I then illustrate how even one domain’s CFPI score for two states allows comparative capability-based signalling analysis that previously would not have been possible. The expression for total CFPI score is as follows:
Focusing on the air domain:
Multirole fighters specifically:
The aforementioned results from expanding the expression for a single system type to include systems at each of the five generational tiers of the CFPI. Tables 4 and 5 list multirole fighter inventories of China and Russia in the year 2023 per the International Institute for Strategic Studies’
Chinese Multirole Fighters, 2023
Platform | Quantity |
---|---|
J-10A/S | 313 |
J-10B/C | 275 |
J-11/B/BS | 297 |
J-16 | 250 |
Su-30M2/MKK/MKI/SM | 97 |
Su-35/BM/S | 24 |
Russian Multirole Fighters, 2023
Platform | Quantity |
---|---|
MiG-29SM | 15 |
MiG-31BM | 107 |
Su-27/B/C | 48 |
Su-27ML/SM/SM3 | 71 |
Su30M2/MKK/MKI/SM | 122 |
Su-35/BM/S | 99 |
Faced with the raw data, an analyst unfamiliar with each platform designation would be limited to unhelpful techniques like comparing the number of multirole fighters in each inventory (an unfortunately common practice). At this point, it is only apparent that China’s 2023 arsenal contained more multirole fighters and that there is some model overlap between the two states. To avoid such underwhelming conclusions, analysts can either abandon the pursuit or commit considerable effort to gaining familiarity with the seemingly endless nomenclatures of conventional weapons. A downside to the latter approach is that the ensuing analysis risks being incomprehensible to its intended audience.
To make comparisons that do not encounter granular barriers to entry, we can score the systems using the CFPI. Table 6 lists a selection of multirole fighters currently coded in the CFPI method found in the arsenals of the United States, China, Russia, North Korea, and Iran with generational tiers resulting from
Multirole Fighters by CFPI Tier
Name | Tier | Name | Tier |
---|---|---|---|
Adir | 4 | JAS 39C/D | 2 |
Barak | 2 | JAS 39E | 3 |
CF-18AM/BM | 2 | JF-17/A/B (Block 1/2) | 2 |
Ching Kuo | 2 | JF-17A/B (Block 3) | 3 |
EF-2000 | 2 | KF-16C/D | 2 |
EF-2000 FGR4/T3 | 3 | MiG-29SM | 2 |
F/A-18 A/B | 2 | MiG-29M/M2/ME | 3 |
F/A-18 C/D | 3 | MiG-31BM | 2 |
F-15E/I/S | 2 | Mirage 2000-5/5F | 2 |
F-15K | 3 | Mirage 2000C/D/E | 1 |
F-15SA | 3 | Mirage 2000H/I | 3 |
F-16C/D Block 25/30/32 | 1 | Mirage F1/E | 1 |
F-16C/D Block 40/42/50/52/+ | 2 | Ra’am | 3 |
F-16V | 3 | Rafale B F3-R/C F3-R | 3 |
F-35/A/I | 4 | Rafale/B/C/DH/DM/EH/EM (F2) | 2 |
F-4D/E | 1 | Saegheh | 2 |
F-4E 2020 | 2 | Su-22 | 1 |
FA-50 | 2 | Su-22M4 | 1 |
FC-1 | 2 | Su-27/B/C | 1 |
FC-20 | 2 | Su-27ML/SK/SM/SM3 | 2 |
F-CK-1A/B | 2 | Su-30/K | 2 |
F-CK-1C/D | 3 | Su-30M2/MKK/MKI/SM | 3 |
Gripen C/D | 2 | Su-35/BM/S | 3 |
J-10A/S | 2 | Su-7 | 0 |
J-10B/C | 3 | Sufa | 2 |
J-11/B/BS | 2 | Tejas | 3 |
J-16 | 3 | Terminator | 2 |
J-6 | 1 | Typhoon | 2 |
JAS 39A/B | 1 | Typhoon FGR4/T3 | 3 |
Reference: U.S. Army Worldwide Equipment Guide.
Using the values in Table 6, we can compute values representing the conventional capability-based signalling afforded Russia and China by each state’s multirole fighters in the year 2023. Tables 7 and 8 demonstrate this.
Chinese Multirole Fighter CFPI Score, 2023
Platform | Quantity | CFPI | |||
---|---|---|---|---|---|
J-10A/S | 313 | 2 | 3 | 32 | 30,048 |
J-10B/C | 275 | 3 | 4 | 32 | 35,200 |
J-11/B/BS | 297 | 2 | 3 | 32 | 28,512 |
J-16 | 250 | 3 | 4 | 32 | 32,000 |
Su-30M2/MKK/MKI/SM | 97 | 3 | 4 | 32 | 12,416 |
Su-35/BM/S | 24 | 3 | 4 | 32 | 3,072 |
Quantity Source: International Institute for Strategic Studies.
Russian Multirole Fighter CFPI Score, 2023
Platform | Quantity | CFPI | |||
---|---|---|---|---|---|
MiG-29SM | 15 | 2 | 3 | 32 | 1,440 |
MiG-31BM | 107 | 2 | 3 | 32 | 10,272 |
Su-27/B/C | 48 | 1 | 1 | 32 | 1,536 |
Su-27ML/SM/SM3 | 71 | 2 | 3 | 32 | 6,816 |
Su30M2/MKK/MKI/SM | 122 | 3 | 4 | 32 | 15,616 |
Su-35/BM/S | 99 | 3 | 4 | 32 | 12,672 |
Quantity Source: International Institute for Strategic Studies.
Having followed the CFPI scoring steps, some more helpful conclusions follow. We could already observe that Russia’s inventory of multirole fighters was considerably smaller than China’s, but we can additionally observe that it is only marginally less technologically sophisticated. The difference between the capability contribution of multirole fighters to the signalling value of each state’s arsenal is then roughly proportional to the numerical difference, a conclusion that we could not make with any real confidence before scoring. Table 9 lists data and scores for the entire air-focused components of Chinese and Russian conventional arsenals in the year 2023.
Comparison of Air-focused CFPI Scores, Russia and China, 2023
CFPI Score | ||
---|---|---|
System Type | Russia | China |
Air Superiority Fighter ( |
3,264 | 36,744 |
Multirole Fighter ( |
48,352 | 141,248 |
Ground Attack Aircraft ( |
57,216 | 26,304 |
Air Defence Missile System ( |
142,320 | 131,472 |
251,152 | 335,768 |
Underlying Quantity Source: International Institute for Strategic Studies.
The data suggest instructive conclusions concerning the two states’ capability basis for air-focused conventional signalling. China’s airpower arsenal exhibits two principal repositories of firepower potential: multirole fighters and air defence missiles. This suggests a relatively even prioritisation of deterrence by unambiguously defensive systems (air defence) and systems whose offensive potential for power projection lends them an ambiguous quality. Russia, on the other hand, has a clear centre of gravity for its air-focused firepower potential: its air defence missile systems. Restricting our consideration for the moment to air-focused CFPI scores, the data do not suggest a robust Russian airpower projection signal relative to that inherent in China’s inventory.
This study’s method aims to enhance the pursuit of capability-based balance of power analysis by enabling estimative comparisons of conventional strategic signalling value of state arsenals, with distinct constraints and possibilities. These include (1) the abstract nature of indexes, (2) the inability to consider unconventional capabilities or systems not listed, (3) the impossibility of using CFPI scoring alone to predict conflict outcomes with any confidence, (4) the risk that changing technology will constrain CFPI’s uses to historical analysis and (5) the possibilities of using CFPI scoring to enhance other avenues of defence analysis.
I simply cannot claim that the CFPI on its own enables anysort of precise measurement of the aggregate quality of state conventional weapon systems; it only improves incrementally on the current state of comparative analysis, which is characterised by a practical inability to make quality-based comparisons between weapons outside methods intended to project their effectiveness in combat with questionable conclusions. Just as gross domestic product (GDP) fails to capture nuances beyond an economy’s size and easily masks sector-specific weaknesses or strengths, the CFPI enables analysts without granular conventional weaponry knowledge to discern the broad contours of capability-based signalling capacity for balance of power analysis. As mentioned previously, the CFPI does not take into account any of the myriad factors needed to operate these weapon systems effectively such as crew availability and skill, ammunition and maintenance.
By its very nature, the CFPI is unable to capture signalling contributions of military systems that are not conventionally armed. These include nuclear platforms (aircraft, submarines, and missile systems primarily intended for nuclear weapons delivery are excluded from CFPI tables), logistical systems that could contribute to strategic signals (particularly large-scale airlift or sealift systems), and mobility systems (mine warfare vessels and vehicles). While these blind spots are understandable given the method’s firepower potential focus and the observation at this study’s outset that most states procure far more combat hardware than their relatively weak logistical systems can support, they are blind spots nonetheless and analyses using the CFPI should appropriately caveat or avoid any ascriptions of intent or capability.
The CFPI is also limited to the availability of quantity and type data for the systems that it scores. Table 3 does not include sophistication descriptors prior to 1990 because quantities in the underlying dataset (IISS’
The CFPI absolutely cannot on its own support conflict outcome prediction with any degree of confidence and even with multiple tools conflict outcome prediction is a fraught pursuit. It may seem ironic that having noted the criticism that has befallen techniques like aggregated firepower score and WEI/WUV, I root CFPI’s tactical role value computation in reductive approximations of explosive yields by index systems releasing index munitions under wholly theoretical conditions. However, I do not propose – and strongly caution against – applying normalised munition yields from CFPI
Developments in military technology, particularly in the area of remote and automated systems, may eventually constrain the CFPI’s current framework of major conventional systems to historical analyses. The proliferation of loitering munitions and the tendency of emerging doctrines toward swarming lethality distributed among growing numbers of smaller systems could signal a reprioritisation of traditional tactical roles even more dramatic than the aircraft carrier eclipsing the battleship during World War II (Atherton 2021; Holmes 2022). Even as I acknowledge this horizon as carrying serious implications for the type of analysis I propose, estimating the proximity of this milestone is well outside the scope of this paper.
These caveats notwithstanding, I believe the CFPI solves real problems facing would-be military balance of power analysts. Accepting the premise that most of the world’s conventional weaponry serves a signalling contribution role most of the time, CFPI scoring represents an accessible proxy for this signalling and in a snap analysis for capability more broadly in the conventional arena. The CFPI can also combine with other concepts to make well-worn avenues of defence analysis more informative.
While the CFPI on its own cannot model engagements and predict outcomes, it may nevertheless serve as a basis for other analysts to augment or develop their own engagement models if they can resolve the deficiencies noted earlier through their own techniques. Tables 2 and 3 represent a novel method of enumerating major conventional weapons. It is entirely possible that other analysts may find this enumeration useful to include in their own richer methods more focused on modelling conventional engagements.
Assuming that when states purchase weapons, they are usually purchasing the capability-based component of conventional signalling capacity, more meaningful analysis of procurement spending becomes possible. Even when procurement spending is disaggregated from total defence spending – a constantly cited figure that typically lacks information to be useful – the inability to make comparisons between state arsenals impedes a full appreciation of procurement analysis.
Although this section and the next focus on CFPI scoring for comparative analysis between states in the same year, the CFPI also enables analysis of state arsenals over multiple years. This may simply describe and compare change over time or support procurement analyses. The change in a state’s CFPI score is expressed:
In this straightforward, recursive expression, a change in the CFPI score is the difference in the CFPI score between the year of analysis
Analysis employing this expression requires longitudinal CFPI scores and procurement spending data, and probably cannot work for states that indigenously produce their weapons (particularly with substantial research and development). The number of years used for a procurement efficiency calculation is not fixed at three as in the previous example but would necessarily vary from state to state and pose challenges of distinguishing procurement spending (that subset of defence spending that is solely used to purchase weapons) from development spending (research, prototyping, testing, evaluation, and so on). Within these constraints is an avenue for comparative efficiency analysis of conventional weaponry procurement by arms-importing states. Assuming procurement spending can be accurately isolated – a challenge given that many states withhold such figures from public release – we may describe states updating their inventories with more competitive systems, faster and over shorter periods of time as procuring more efficiently compared to other states. Comparative analysis thus does not require selection of the optimal time interval for each state but simply application of the same time interval to both (or all) states being compared.
The United States released the most recent version of its statutorily mandated National Defense Strategy (NDS) in 2022. The opening to the unclassified summary reads in part:
Does a comparative analysis of the approximate signalling value of the Chinese, Russian, North Korean, and Iranian conventional arsenals offer insight into the aforementioned ‘challenge’, ‘aggression’ and ‘threats’? What do apparent conventional postures of each state suggest for ‘linkage between strategy and resources’? In this section, I use the CFPI scoring to examine the premises and conclusions of the NDS in ways that would be difficult or misleading without structured comparative analysis of capacity-based conventional strategic signals.
Before presenting CFPI results, I visit
The
Site rankings put the United States first overall, with Russia a close second and China a close third. As
The aforenoted issues do not dissuade citation of
Oleksiy Kuzmenko’s reporting indicates serious security scholars and defence analysts either have not heard of
I focus on depicting comparative results of CFPI scoring for the United States, China, Russia, North Korea and Iran using arsenal data from the 2023 edition of the IISS’
Comparison of Conventional Firepower Potential Indexing Scores, U.S., China, Russia, North Korea, and Iran, 2023.
System Type† | CFPI Score | ||||
---|---|---|---|---|---|
United States | China | Russia | North Korea | Iran | |
Air Superiority Fighter | 41,040 | 36,744 | 3,264 | 7,776 | 3,840 |
Multirole Fighter | 236,800 | 141,248 | 48,352 | 0 | 3,104 |
Ground Attack Aircraft | 30,816 | 26,304 | 57,216 | 2,736 | 2,088 |
Air Defence Missile System* | 86,040 | 131,472 | 142,320 | 8,880 | 12,144 |
Main Battle Tank* | 58,840 | 97,600 | 42,000 | **42,000 | 10,760 |
Armoured Fighting Vehicle* | 336,676 | 132,680 | 56,640 | 4,824 | 5,380 |
Self-Propelled Cannon Artillery* | 4,992 | 17,660 | 4,436 | 8,600 | 584 |
Towed Cannon Artillery* | 5,985 | 800 | 2,090 | 2,150 | 1,840 |
Rocket Artillery* | 5,715 | 10,500 | 3,852 | 9,435 | 828 |
Rotary Wing Attack* | 63,040 | 19,072 | 13,488 | 0 | 800 |
Multirole Rotary Wing* | 33,736 | 6,756 | 2,884 | 1,144 | 640 |
Air Defence Gun System* | 0 | 1,446 | 210 | 2,750 | 572 |
Surface-to-Surface Missile* | 5,136 | 27,000 | 12,000 | 2,664 | 11,520 |
Aircraft Carrier (Nuclear-powered) | 103,680 | 0 | 0 | 0 | 0 |
Aircraft Carrier (Non-nuclear-powered) | 28,188 | 6,804 | 972 | 0 | 0 |
Helicopter Carrier | 3,888 | ***3,888 | 0 | 0 | 0 |
Ship-Based Rotary Wing* | 20,610 | 1,068 | 858 | 0 | 78 |
Ship-Based Fixed Wing | 122,912 | 7,680 | 3,360 | 0 | 0 |
Shore-Based Maritime Fixed Wing* | 11,328 | 5,160 | 1,728 | 0 | 72 |
Cruiser | 28,350 | 12,600 | 2,250 | 0 | 0 |
Destroyer | 67,200 | 45,300 | 3,300 | 0 | 0 |
Frigate | 12,320 | 17,220 | 6,720 | 280 | 0 |
Corvette* | 0 | 12,000 | 12,000 | 0 | 1,140 |
Missile Boat/Fast Attack Craft* | 360 | 4,416 | 0 | 984 | 1,800 |
Tactical Submarine(Nuclear-powered) | 48,600 | 6,300 | 13,500 | 0 | 0 |
Tactical Submarine (Non-nuclear-powered)* | 0 | 9,360 | 2,400 | 3,600 | 960 |
Shore-based Anti-ship Missile* | 0 | 17,310 | 6,240 | 540 | 1,620 |
447,436 | 149,106 | 53,328 | 5,404 | 5,670 | |
1,356,252 | 798,388 | 442,080 | 98,363 | 59,770 |
Underlying quantity source: International Institute for Strategic Studies.
Systems are classified according to international convention reflected in IISS’
Indicates systems excluded from extra-regional projection CFPI score (see Figure 5).
Russia’s 1,850-strong blend of assorted variants of T-62, T-72, T-80 and T-90 and North Korea’s 3,500-strong blend of Chonma,Pokpoong, Songun, T-34, T-54, T-55, T-62 and Type 59 each scored 42,000 in 2023.
China’s 11-strong fleet of Type 071 and Type 075 and the United States’ 12-strong San Antonio-class fleet each scored 3,888 in 2023.
It is immediately apparent that the CFPI suggests a dramatically different set of capabilities contributing to conventional strategic signals compared to the popular
The data of the preceding charts enable us to revisit the 2022 U.S. NDS’ premises. Rather than embarking on an in-depth analysis of each chart – the aim of this article is to contribute the CFPI method and illustrate possibilities, not a deep-dive into the NDS’ outlook – I briefly distil insights into the magnitude and nature of the cited threats and prospective investments.
CFPI scoring combined with readily available macroeconomic data suggests that if any country can realistically contemplate future conventional parity with the United States, it is China. Even this popular projection is callsed into question by China’s economic indicators suggesting signs of stagnation with its gross domestic product at approximately 70% that of the United States (World Bank Open Data Tool; Sharma 2022; Stokes 2023).
The yawning gap in conventional posture incentivises the other name-checked states to pursue unconventional advantages. For North Korea and Iran, nuclear arms represent an attractive insurance policy. Russia, with its legacy ability to advance a prestige narrative by showcasing some premier capabilities, is nonetheless also incentivised to exploit capabilities in the difficult-to-at-tribute realms of offensive cyber and disinformation operations (Cunningham 2020; Lilly and Cheravitch 2020). Comparative advantage for Russia in unconventional arenas is further heightened because of conventional losses incurred during its full-scale invasion of Ukraine (Watling et al. 2023). Reports of Russia’s collaboration with both Iran and North Korea underscore the proclivity of these three countries to seek unconventional competitive advantages (Geranmayeh and Grajewski 2023; Regan et al. 2023).
While the United States is free to pour resources into politically popular and technically straightforward efforts to further bolster conventional advantage, the reality is that America’s arsenal uniquely postures it to send robust extra-regional extended conventional deterrent signals. This means that investment in conventional capabilities – while necessary if the U.S. prioritises a conventional posture edge over China – probably crosses a point of diminishing returns given the extant capability gaps and the astronomical price tags of advanced air and naval systems. The most lucrative avenue for the U.S. to link strategy and resources to keep China’s capability-based posture in check may be to arm allies in the region; see Australia’s abandonment of longtime strategic ambiguity in agreeing to receive nuclear-powered submarines from the United States implicitly to balance China (Pei 2021).
Setting aside the largely diplomatic challenges of managing nuclearisation by North Korea and Iran, CFPI scoring suggests that dollar for dollar, more promising applications for increased and sustained investment lie in counter-cyber and counter-disinformation measures. An irregular warfare annex to the previous (2018) NDS particularly noted Russia’s proclivity toward and proficiency with disinformation and cyber operations, which suggests that at least some within the Pentagon share this perspective (U.S. Department of Defense 2020).
This all confines the scope of the CFPI scoring-informed analysis to threats cited by the NDS. Other voices argue climate change and pandemics represent risk sources that would benefit from some share of U.S. spending otherwise pouring into extending already wide conventional advantages. If comparative arsenal analysis represented a great enough challenge to justify the writing of this paper, devising a framework for fiscal value judgments across completely disparate realms of policy justifies authorship of multiple libraries of books.
This study set out to identify a problem and propose some degree of solution. Conceiving the problem as the existence of extensive obstacles to meaningful, accessible comparative conventional arsenal analysis and the proclivity of journalists and governments to cite non-credible sources in the absence of credible ones, the solution is adopting a clear if reductive framework with modest goals to enable comparative conventional armament posture analysis. By avoiding conflict outcome prediction and focusing on the capability component of conventional strategic signals suggested by arsenal compositions, I believe this CFPI contributes some new methodological good to the field.
I look forward to exploring and improving the method by employing it in more systemic and longitudinal investigations, including two ongoing projects: (1) an Indo-Pacific-focused time-series analysis of the CFPs of Australia, China, India, Japan, South Korea, Taiwan, and the United States; and (2) the application of CFPI analysis to gain better insight into the capability attrition incurred by Russia since the start of its full-scale invasion of Ukraine.