Carbon Emission Assessment and Reduction Pathways of Teaching and Research Equipment in Application-Oriented University in China Based on Life-Cycle Analysis

Abstract

In this study, 7647 scrapped pieces of teaching and research equipment (T&R equipment) in an application-oriented university in China in 2024 were employed to assess their carbon emissions using lifecycle analysis. A lifecycle accounting framework was established based on expenditure–environmental expansion input–output (EEIO) models, and the greenhouse gas emissions across the producing, using, and scrapping stages of the T&R equipment at this type of university were estimated. Carbon emission reduction pathways for T&R equipment at this type of university were proposed. It is clear that the lifecycle emissions of the scrapped equipment at this type of university equal 8350.8 tCO₂, including producing, using, and scrapping stage emissions of 2277.9 tCO₂, 5848.9 tCO_2, and 223.9 tCO₂, respectively. It is noted that the producing stage accounts for the dominant contributor to carbon emissions, with 70.0% of the total amount. In view of subcategory emissions, information technology equipment (A0201) contributes the most emissions, with 18.0% during the producing and scrapping stages, whereas instruments (A0210) and electrical/electronic production equipment (A0233) contribute the most, with 21.4% and 15.7%, in the using stage. The results of scenario analysis show that, for most equipment, total carbon emissions can be reduced by about 233 tCO₂/a on average if scrapped one year in advance. However, for information technology equipment (A0201), emissions increase by 48 tCO₂/a. This method offers comparability and replicability in scenarios lacking physical measurements, providing quantitative evidence and carbon reduction pathways for green procurement, asset renewal, and end-of-life recycling in higher education institutions.

1. Introduction

Global warming has led to a severe threat to human survival [1,2,3]. Increased concentrations of carbon dioxide in the atmosphere have intensified the greenhouse effect, leading to a sustained rise in global average temperatures [4,5,6]. Universities are considered significant social organizations, with high energy consumption and carbon emissions due to their large populations and extensive infrastructure [5,7,8]. China’s Ministry of Housing and Urban–Rural Development reported that the annual total energy consumption of Chinese universities exceeded 20 million metric tons of standard coal equivalent in 2021. This value accounts for 8.5% of the nation’s total residential energy consumption [9]. It is noteworthy that the average annual carbon emissions per student are approximately five times higher than the national average [7]. Therefore, promoting the achievement of carbon peak and carbon neutrality goals among higher education institutions holds significant strategic importance. China has a large higher education system.

There were 3119 higher education institutions, with 48.46 million undergraduates, in China in 2024 [10]. Application-oriented universities enrolled one out of three undergraduate students and exceeded 1000 undergraduate students in total, accounting for 80% of all undergraduate colleges and serving as a vital force in driving China’s higher education toward leapfrog development and advancing its universalization process [11], which also resulted in higher energy consumption and carbon emissions. Many studies have been conducted to identify the key drivers of campus carbon emissions [12,13,14] and explore feasible emission reduction pathways [15,16,17]. For example, among other studies, Wang et al. used a combination of LEAP and LCA to assess the carbon emissions of a medium-sized university in eastern China to be 13,877 tons of CO₂-eq in 2020, and proposed corresponding emission reduction recommendations, while Raeanne et al. measured the total carbon footprint of Clemson University to be about 95,418 tons of CO₂-eq based on top-down estimation of the emission factors and analyzed the university’s carbon emission composition [18,19,20,21,22]. However, these studies, based on the GHG Protocol system, mainly focused on Scope 1 (direct emissions) and Scope 2 (indirect emissions from purchased electricity and heat), and generally failed to adequately consider the completeness of Scope 3 emissions, especially ignoring the growing fixed asset-related emissions, thus possibly leading to an underestimation of the university’s overall carbon emissions level.

It has been reported by Wind the fixed assets in China’s higher education sector grew in 2013 to 2020 from 1.5161 trillion yuan to 2.7375 trillion yuan, achieving a compound annual growth rate (CAGR) of 8.81% [23]. Moreover, in the future, national policies such as the Action Plan for Promoting Large-Scale Equipment Renewal and Consumer Goods Replacement will further explicitly support universities in upgrading “advanced teaching and research technology equipment,” while mandating the implementation of subject-specific teaching equipment configuration standards [24]. This is the other factor that intensified the potential pressure of equipment-related carbon emissions at China’s applied universities [25]. Therefore, scientifically quantifying the carbon emissions of university equipment in its entire lifecycle not only aligns with the national strategy of strengthening resource recycling, but also provides essential theoretical foundations and data support for low-carbon management of equipment in China’s applied universities [26,27,28]. However, traditional carbon emission calculation methods based on physical mass are facing significant challenges in practical application, due to their high dependence on comprehensive material lists and product weight data with a complicated operational process, being limited its applicability in university settings characterized by diverse assets and incomplete data [29,30].

In this study, a method for calculating the lifecycle carbon emissions of university fixed assets based on the amount of money paid for the assets was proposed. The method established a calculation pathway from expenditures to emissions by combining financial data with an environmentally extended input–output (EEIO) model. As a result, the accounting efficiency and practical feasibility were both improved. The study aims to provide scalable technology for establishing systematic, standardized carbon management mechanisms for application-oriented universities in China.

2. Method

2.1. Research Scope

A typical application-oriented university undergoing rapid development located in Fujian province was employed as a demonstration university (Figure 1). The university has set the strategic goal of establishing itself as a nationally leading high-level application-oriented university in 2035 and scrapped 7647 pieces of teaching and research equipment (This application-oriented university is located in Putian City, Fujian Province, China. The university’s teaching and research equipment is hereinafter referred to as T&R equipment) in 2024. In accordance with the national standard “Basic classification and codes of fixed assets and other assets” (National Standard of the People’s Republic of China; GB/T 14885-2022 [31]; Ministry of Finance of the People’s Republic of China; 2022 [10]), the scrapped T&R equipment was classified into four major categories (including laboratory instruments and information technology equipment, cultural relics and exhibits, books and archives, and furniture and utensils) with 41 subcategories (Table S1). The physical characteristics, purchase amounts and residual net values of various items were precisely adapted.

2.2. Calculation Method for Full Lifecycle Carbon Emissions of T&R Equipment

As shown in Figure 2, the carbon emissions across the full lifecycle of T&R equipment were categorized into three stages including the producing stage (encompassing processes such as raw material acquisition, component manufacturing, transportation, and distribution, i.e., the “cradle-to-gate” stage), the usage stage (involving the operational energy consumption of the T&R equipment during annual and entire lifecycle operations) and the scrapping (end-of-life) stage. The detailed research methodology flow is shown in Figure S5.

2.2.1. Calculation Method for Carbon Emissions During the Production Stage

The carbon emissions of T&R equipment during the production stage were estimated employing a spend-based approach combined with industry-specific greenhouse gas emission intensities obtained from the environmentally extended input–output (EEIO) database. The basic logic of the expenditure method is that the amount of expenditure on asset purchases is positively correlated with the emissions of its whole life cycle supply chain, so the corresponding carbon emissions can be obtained by amount × industry emission intensity. This method provides a practical pathway for estimating carbon emissions of large-scale T&R equipment in the absence of detailed physical quantity data, such as product weight and material composition.

Industry emission intensity factors are selected from the USEEIO v1.2 Supply Chain Greenhouse Gas Emission Factors database published by the U.S. Environmental Protection Agency (U.S. EPA). This database, based on multi-regional input–output tables and greenhouse gas inventories, provides industry greenhouse gas emission intensities measured in kgCO₂/USD (Purchaser Price). This intensity value reflects the greenhouse gas emissions associated with each unit of currency spent on final consumption throughout the supply chain lifecycle, encompassing stages such as raw material production, manufacturing, transportation, and distribution. Considering the differences in implied emissions across various categories of T&R equipment, this study classified and mapped the sample based on national standard major/sub-major categories and asset names. Map the industry emission intensity factors to corresponding sectors in the EEIO database. For example, “information technology equipment” maps to Computers, “furniture” maps to Furniture Manufacturing, and “vehicles” maps to motor vehicle manufacturing. For categories without direct equivalents in the EEIO database, use the closest functional or process-based substitute (Text S1 and Table S2 for details).

To ensure consistency in monetary terms, this paper converts the original RMB amounts into US dollar base-period prices aligned with EEIO intensity using exchange rates and price indices. Carbon emissions during the production phase of T&R equipment products are calculated using the following formula:

$$\begin{array}{c}{E}_{i,produce}={AD}_i\times {EF}_i\end{array}$$

(1)

where $E_{i,produce}$ represents the carbon emissions (kgCO₂) from assets during the production phase of the i-th subcategory product; ${AD}_i$ represents the amount of the i-th subcategory asset (converted to base-period USD via exchange rates and price indices); ${EF}_i$ represents the EEIO emission intensity (kgCO₂/USD) for subcategory i to which the asset belongs.

2.2.2. Calculation Method for Carbon Emissions During the Usage Stage

To calculate the carbon emissions of T&R equipment during its usage stage, this study employs the following methodology. First, by reviewing each subcategory of T&R equipment, obtain its rated energy consumption (unit: kWh/h) and use this data to calculate the average energy consumption value for each subcategory. Second, regarding the average annual usage duration, a combination of online surveys and offline inquiries was employed to collect actual operational data and determine a reasonable annual estimate through comprehensive analysis. Electricity emission factors uniformly adopt the Fujian Provincial Grid emission factors published by the national authorities [32]. For detailed parameter specifications and rated energy consumption data, refer to Table S3. Carbon emissions during the T&R equipment usage stage are calculated using the following formula:

$$\begin{array}{c}{EY}_{year}={EC}_i\times {AU}_i\times Q_i\end{array}$$

(2)

$$\begin{array}{c}{E}_{i,year}={EY}_{year}\times {EF}_{Electricity}\end{array}$$

(3)

$$\begin{array}{c}{EY}_{lifespan}={EC}_i\times {AU}_i\times Q_i\times L_i\end{array}$$

(4)

$$\begin{array}{c}{E}_{i,lifespan}=E_{lifespan}\times {EF}_{Electricity}\end{array}$$

(5)

where i represents the i-th subcategory, year represents the average annual value, and lifespan represents the T&R equipment’s life cycle; ${EC}_i$ is the average energy consumption, kWh/h; ${AU}_i$ represents annual usage hours, h/year; $Q_i$ represents quantity, pieces; ${EY}_{year}$ represents annual electricity consumption, kWh; $E_{i,year}$ represents the annual average carbon emissions, kgCO₂; ${EF}_{Electricity}$ represents the grid emission factor, kgCO₂/kWh; $E_{lifespan}$ represents the total electricity consumption over the product’s lifespan, kWh; $E_{i,lifespan}$ represents carbon emissions over the product’s lifetime, kgCO₂.

2.2.3. Calculation Method for Carbon Emissions During the Scrapping Stage

According to the GHG Protocol Scope 3 Technical Guidance, when original activity data is unavailable, the expenditure-based emission factor method may be used for estimation. The emission factor for “end-of-life disposal processes only” should be selected to reflect carbon emissions generated from waste management. Under this framework, the scrapping of T&R equipment is treated as the purchase of a waste management service. The carbon emissions for this stage are calculated by multiplying the corresponding emission factor (kgCO₂/currency unit) by the actual expenditure incurred during the scrapping stage.

Due to the fact that actual disposal expenditures at China’s applied universities are typically excluded from unified statistical reporting, this study employs a conservative and operationally feasible alternative approach. A default disposal cost ratio (%) is assigned to each sub-industry. Based on this ratio, the cost incurred during the scrapping stage is estimated from the procurement amount. This default disposal cost ratio is derived from empirical values obtained by consulting experts from waste recycling companies. Combined with the emission intensity factor for the waste management services industry, the final carbon emissions during the scrapping stage are calculated. The relevant parameters and detailed data are listed in Table S4. Carbon emissions during the T&R equipment scrapping stage are calculated using the following formula:

$$E_{i,scrape}=A\times r_i\times {EF}_{wms}$$

(6)

where i represents the i-th subclass; $E_{i,scrape}$ represents carbon emissions during the scrapping stage, kgCO₂; A represents the procurement amount of the equipment (in the same currency/price base period as the factor), USD; $r_i$ represents the proportion of the default disposal cost, %; ${EF}_{wms}$ represents the expenditure factor (kgCO₂/USD) for the Waste Management Services industry.

2.3. Emissions Inventory Uncertainty Analysis

The uncertainty in emission inventories is primarily driven by activity data and emission factors [33,34]. In this study, the Monte Carlo method was employed to assess the uncertainty in emission inventories. This method serves as an effective and versatile tool that has been widely applied in previous emission inventory studies [35,36]. By assuming standard deviations and probability distributions for parameters, 10,000 Monte Carlo simulations were run to generate the estimates. The results of the methodological uncertainties in this study are presented in the data in the Section 3. The detailed parameter uncertainty setting values and Monte Carlo outputs are detailed in Text S2 and Figures S1–S4.

3. Results and Discussion

3.1. Basic Characteristics of T&R Equipment

The service life, original value and net value of categories of 7647 scrapped T&R equipment are shown in Figure 3.

It is evident that the terms of service life, original value and net value are contingent on the type of T&R equipment employed. First, examining the average service life across the four T&R equipment categories (Figure 3a,d), significant differences emerge: furniture and utensils (A05) exhibit the longest average service life of 20.6 years; equipment (A02) has the shortest service life of 13.5 years only; cultural relics and exhibits (A03) and books and archives (A04) are relatively close at 14.5 years and 14.3 years, respectively. Further subcategory analysis reveals that exploration/mining/ore processing equipment (A0213) has the highest average service life among equipment categories, reaching 25 years. Verification of traceability indicates that this asset class is predominantly constituted by electric motor equipment, whose substantially extended service life may be ascribed to its comparatively infrequent actual usage frequency. It has been demonstrated that the relatively infrequent occurrence of relevant experimental courses (with an average frequency of ≤4 times per year) results in equipment operating loads that fall far below design thresholds. This, in turn, has a beneficial effect on the extension of the service life of the equipment. In the domain of cultural relics and exhibits (A03), the service life of human/biological models (A0305) exhibits considerable variation, with some models maintaining functionality for up to 20 years. This considerable variability is principally attributable to variations in instructional tasks. Anatomical models, due to their frequent utilization, have an average lifespan of only 8.2 years; conversely, biological specimens employed for display purposes can be preserved in the long term under standardized storage conditions, frequently exceeding 20 years of service.

From the distribution of equipment purchase amounts and net values (Figure 3b,c,e,f), the equipment category (A02) exhibits a highly skewed distribution. Most samples cluster in the lower amount range (<5000 CNY), while a few high-value equipment items elevate the mean (average 6641 CNY). But its net value is relatively low, with an average of only 85 CNY, and the residual value amounts to just 1.28% of the original amount. The second-highest acquisition expenditure was for cultural relics and exhibits (A03), which maintained relatively stable acquisition costs and a higher net value retention rate (greater than 99%). Books and archives (A04) also exhibited high net value retention, with acquisition costs nearly matching their net value. For furniture and utensils (A05), sample dispersion was relatively high, primarily influenced by fluctuations in fixtures (A0503). The average purchase amount was 719 CNY, with a net value of only 10 CNY remaining—representing a residual value of just 1.44% of the original amount.

Overall, systematic differences exist across T&R equipment categories in terms of lifespan, acquisition cost, and net value. Furniture and fixtures demonstrate relatively balanced characteristics, exhibiting longer lifespans, moderate acquisition costs, and low net value retention. Library and archival assets share similar lifespans with cultural relics and exhibits, featuring low acquisition costs but high net value retention. Equipment assets represent the largest category, yet exhibit high dispersion in both acquisition cost and net value. A small number of high-value items significantly skew the mean, while the overall depreciation rate is extremely high.

3.2. Carbon Emissions Analysis of T&R Equipment During the Production Stage

The Expenditure-EEIO method is utilized in this study to calculate carbon emissions during the production stage of the T&R equipment products. The results are then aggregated and compared by asset category and subcategory. As illustrated in Figure 4b,c, the unit product carbon intensity and total distribution are presented for the four primary categories (A02, A03, A04, A05). From an intensity perspective, the production-per-stage carbon emission intensities for the four categories were 0.39, 0.073, 0.063, and 0.047 ton/unit, respectively, in descending order: equipment (A02) > cultural relics and exhibits (A03) ≈ books and archives (A04) > furniture and utensils (A05). The observed discrepancy can be attributed to systemic disparities across diverse product categories, manifesting in variations in material composition, manufacturing intricacy, and energy consumption within the upstream supply chain.

The production stage of the equipment was responsible for the total emissions of 2277.90 tCO₂, with a 95% confidence interval [2027.94, 2533.47]. The structure demonstrates a substantial concentration at the top. Equipment (A02) was responsible for the total emissions of 2171.44 tCO₂, with a 95% confidence interval [1781.01, 2580.95], constituting 95.3% of the total. This category is almost entirely responsible for the scale of emissions during the production stage. Among these, information technology equipment (A0201) was identified as the primary source of emissions, with a total of 1401.97 tCO₂, and a 95% confidence interval of [1152.56, 1673.25]. This accounted for 64.56% of the emissions from the equipment category (A02). This prominence stems from both the higher proportion of resource- and energy-intensive processes involved in the early stages of information technology equipment lifecycles, and the fact that it had the highest number of units scrapped in this sample (1989 items).

In comparison, the furniture and utensils category (A05) emitted a total of 84.97 tCO₂, with a 95% confidence interval [69.78, 100.72], accounting for 3.7% of total production stage emissions. This was comparable to the 90.56 t emitted by office equipment (A0202), with a 95% confidence interval [73.96, 107.29]. This is due to the fact that the quantity of discarded furniture and utensils (A05) (1806 items) significantly exceeded that of office equipment (480 items), indicating that unit intensity was the key factor driving the narrowing gap in their total quantities. The total emissions for the cultural relics and exhibits category (A03) and the books and archives category (A04) were 11.19 tCO₂ (95% CI [8.29, 12.03]) and 10.30 tCO₂ (95% CI [8.39, 12.19]), respectively, representing comparable scales. Based on the analysis of the characteristics of these two asset categories in the previous section, it was found that they share similarities in durability and material properties. Furthermore, examining the subcategories within the three major categories of A03, A04, and A05, revealing a similar pattern where contributions are concentrated in a few subcategories. For instance, models (A0305) and furniture (A0501) stand out within their respective scopes, further validating the robustness of top-tier drivers.

It is important to note that the distribution of quantities does not align with the distribution of emissions. It is evident that categories which exhibit a substantial proportion of units do not invariably correspond to the categories with the highest emissions. This finding suggests the presence of considerable heterogeneity in the carbon intensity per unit product across the various categories. Consequently, linear extrapolation based solely on quantity or economic value may lead to underestimation or overestimation of key categories’ contributions. Stratified management must be implemented by accounting for these intensity differences.

In light of these findings, emission reduction efforts should prioritize A0201 (Information technology equipment). The recommendations are focused on two main areas: (1) extending service life and promoting reuse through refurbishment, cross-departmental allocation, and “old-for-new” replacement to prolong T&R equipment lifespan. Prioritize replacing T&R equipment with low embodied carbon but high electricity consumption, while maximizing the service life of equipment with high embodied carbon. (2) Low-carbon procurement: Incorporate product carbon footprint (PCF), recycled material content, and repairability/upgradability requirements into procurement processes, prioritizing models that are modularly upgradable and more recyclable. In summary, this study reveals that embodied carbon emissions from scrapped T&R equipment exhibit extreme concentration within a few categories. Focusing on these key areas can achieve highly cost-effective reductions in embedded carbon emissions within the T&R equipment sector.

3.3. Carbon Emissions Analysis of T&R Equipment During the Usage Stage

As demonstrated in Figure 5, the carbon emissions of T&R equipment during the annual usage stage and over its entire service life were 417.1 tCO₂ (95% CI [381.61, 453.24]) and 5848.9 tCO₂ (95% CI [5310.98, 6411.43]), respectively. A comparison of annual carbon emissions during the usage stage with cumulative emissions over the entire lifecycle reveals significant heterogeneity among different T&R equipment categories. This variation is driven by the dual factors of “power × annual operating hours” and “annual operating hours × service life.” Overall, annual emission levels exhibit a consistent ranking trend with lifetime emission scales; however, influenced by factors such as service life and operating duty cycle, the carbon emission contribution of certain low-power but long-running T&R equipment is amplified when assessed over their lifetime.

With regard to annual emissions, instrumentation and meters (A0210) and electrical and electronic production equipment (A0233) made significant contributions, at 115.2 tCO₂ (95% CI [92.47, 139.19]) and 101.9 tCO₂ (95% CI [82.29, 123.20]), respectively, thus classifying them as high-emission equipment annually. This is primarily because the category of instruments and meters (A0210) includes projectors, access control systems, and similar equipment, while the category of electrical and electronic production equipment (A0233) encompasses devices such as water heaters and air conditioners. Not only are these subcategories quite numerous, but their operating hours and energy consumption levels are also relatively high. Information technology equipment (A0201), mechanical equipment (A0205), beverage processing equipment (A0225), and railway transportation equipment (A0242) form the second-highest emission tier, with emissions of 72.6 (95% CI [58.39, 87.97]), 40.5 (95% CI [32.60, 48.96]), 26.8 (95% CI [21.63, 32.38]), and 15.6 tCO₂ (95% CI [12.53, 18.83]). These subcategories typically exhibit higher energy consumption per unit time, greater annual usage intensity, or larger equipment scale, resulting in higher emission levels.

With respect to lifetime cumulative emissions, information technology equipment (A0201), beverage processing equipment (A0225), and electrical and electronic production equipment (A0233) exhibited cumulative emissions of 893 (95% CI [706.43, 1096.61]), 332 (95% CI [262.88, 407.82]), and 1313 tCO₂ (95% CI [1041.82, 1616.71]), respectively. In a comparison of the annual emission performance, these subcategories exhibit a reduced relative contribution to lifetime emissions, primarily due to their generally shorter service life compared to T&R equipment in other categories. Consequently, the early retirement of such equipment helps reduce carbon emissions generated during the T&R equipment usage stage.

In summary, when formulating emission reduction strategies for the use stage of T&R equipment, priority should be given to identifying high-power, high-operating-hour, or high-performance T&R equipment to pinpoint the structural characteristics of major emission contributors. Simultaneously, a priority sequence for replacement and decommissioning T&R equipment should be established within the lifecycle dimension. This will construct an integrated energy consumption and carbon management pathway encompassing “annual control and lifecycle optimization.”

3.4. Carbon Emissions Analysis of T&R Equipment During the Scrapping Stage

As demonstrated in Figure 6, the total carbon emissions during the scrapping stage of the T&R equipment amounted to 223.9 tCO₂ (95% CI [202.41, 245.54]), exhibiting characteristics similar to a Pareto distribution. Among these, A0201 was the dominant category, accounting for 101.9 tCO₂ (95% CI [83.58, 121.00]), representing 45.49% of total emissions; A0210 and A0232 ranked second and third, with emissions of 46.8 tCO₂ (95% CI [38.15, 55.46]) and 27.3 tCO₂ (95% CI [22.33, 32.26]), respectively. Emissions from other subcategories were relatively dispersed and generally low, mostly falling within the 0~10 tons range. Accordingly, the top three subcategories collectively accounted for 78.6% of total emissions, further confirming the Pareto structure where a few key categories dominate overall emissions. This structural characteristic primarily stems from the influence of carbon accounting methodologies. When employing the expenditure-based approach (EEIO) based on purchase amounts to calculate carbon emissions during the scrapping stage, the prominent performance of high-emission subcategories (such as A0201, A0210, A0231) typically originates from their historically large procurement values and/or relatively high assumed disposal costs. Under the effect of a uniform disposal service intensity coefficient, this disparity is further amplified. Conversely, long-tail categories exhibit a comparatively negligible contribution to emissions, attributable to their minimal expenditure in terms of procurement costs and disposal expenses.

It is evident that the concentration of carbon emissions during the scrapping stage of T&R equipment exhibit significant concentration at the top end. Categories A0201, A0210, and A0231 constitute the primary emission sources, collectively determining the fluctuation characteristics and reduction potential of carbon emissions during this stage. In the short term, it is recommended that management resources be focused on the aforementioned three categories of critical equipment, promoting the establishment of targeted recycling and reuse mechanisms, such as equipment refurbishment or remanufacturing. In the medium to long term, efforts should be directed toward improving a comprehensive lifecycle management system and recycling framework covering all T&R equipment, thereby enhancing the comparability of accounting results and the traceability of audit processes.

3.5. Carbon Emissions Analysis of T&R Equipment Throughout the Full Lifecycle

As shown in Figure 7, from a life cycle perspective, the total carbon emissions of the T&R equipment were 8350.8 tCO₂ (95% CI [7541.33, 9190.44]). Based on an estimated 3119 universities in 2024, the total lifecycle carbon emissions from T&R equipment in China’s applied universities amount to approximately 26.6 million tCO₂, roughly equivalent to the total carbon emissions from gasoline and diesel vehicles in Fujian province during the same term [37]. As shown in Figure S6, this study further analyzed the sensitivity of the life cycle carbon emissions of T&R equipment (at the stages of production, usage, and scrap) with respect to the three key parameters: emission factor, acquisition cost, and life cycle electricity consumption. The results showed that when the above parameters are individually increased by 10%, the life cycle carbon emissions of the T&R equipment will increase by 8.45%, 2.83% and 6.19%, respectively. Among them, the change of emission factors had the most significant effect on the total carbon emissions.

This study focused on the life cycle carbon emissions of T&R equipment, which have not previously been considered in the relevant literature. To verify the accuracy of the calculation results, this paper selected five universities with a similar logic to carbon emission accounting in this study and standardized their carbon emission data according to student size for comparison. This is shown in Table S5. Specifically, Putian University (with a student population of 21,000) was taken as the benchmark and the carbon emissions of the other universities were converted into a comparable unit according to their student population proportions. It was then calculated that the life cycle carbon emissions from T&R equipment accounted for between 3.6% and 16.8% of the total emissions of other research universities. The results indicated that carbon emissions from T&R equipment constituted a non-negligible part of the overall carbon emissions of the campus, which was in line with the expectation of a partial contribution.

During the entire lifecycle of T&R equipment, the use stage emitted the highest carbon emissions, reaching 5848.9 tons, accounting for 70.04% of the total. When allocated to an annual scale, the average annual carbon emissions during the use stage amounted to 417.1 tons, representing 5% of the total lifecycle emissions and 7.1% of the cumulative emissions during the use stage. In terms of T&R equipment categories, the A02 equipment category was a major emission source throughout the product’s production, usage, and scrapping stages. In terms of T&R equipment categories, the A02 equipment category was the major emission source throughout the product’s production, usage, and scrapping stages. During the use stage, all emissions originated from A02 equipment, except for minimal emissions (0.09 tons) from the A0305 model category. Further analysis by subcategory reveals that during the production and scrapping stages, A0201 information technology equipment accounted for the highest emissions, representing 16.8% and 1.2% of total emissions, respectively. In the usage stage, emissions primarily originated from A0210 instruments and meters and A0233 electrical and electronic production equipment, contributing 21.4% and 15.7% of total emissions, respectively.

In accordance with the previously referenced emission structure, differentiated emission reduction priorities should be established for different stages: the use stage should be the primary focus for emission reduction, followed by the product production stage. However, as analyzed earlier, emission reductions in the use stage often depend on the early retirement of T&R equipment, while reductions in the product production stage tend to favor delayed retirement, creating a certain strategic conflict between the two approaches. To address this, Figure 8a further analyzes the impact of early retirement on emission reduction effectiveness for different categories of T&R equipment and their key subcategories across various stages. The findings of this study indicate that, in terms of the combined emissions from product production and use stages, early retirement generally contributed to reducing carbon emissions, with each year of early retirement reducing emissions by an average of 233 tCO₂. However, at the key subcategory level (illustrated in Figure 8b–d), the performance of A0201 Information Technology Equipment differs from that of A0210 and A0233. Its early retirement might instead lead to increased carbon emissions, averaging approximately 48 tCO₂ per year. Comparing A0210 with A0233 reveals that the higher the proportion of emissions during the usage stage, the more significant the carbon reduction effect achieved through early retirement.

Consequently, from a full life cycle perspective, carbon reduction strategies for T&R equipment in China’s applied universities should be differentiated by type: For subcategories with high usage frequency and significant emissions during the use stage (such as A0210 and A0233), prompt retirement is recommended when they malfunction or become damaged. Conversely, for subcategories with high initial investment and substantial emissions during the production stage (e.g., A0201), strategies to extend service life should be prioritized. This process encompasses maintenance and reuse, with the objective of minimizing the full life cycle carbon footprint.

4. Conclusions

Three stages of carbon emissions, including those of producing, usage and scrapping of 7647 T&R equipment in an application-oriented university in 2024, were calculated via the full lifecycle accounting framework established by combining expenditure-EEIO methods. Basically, carbon emission reduction pathways were proposed for each stage of various T&R equipment. The following conclusions are drawn from the evidence presented.

• The full lifecycle carbon emissions of 7647 scrapped T&R equipment are 8350.8 tCO₂ in the demonstrated university and are 26.6 million tCO₂ in all application-oriented universities in 2024 in China. The usage stage contributes the highest proportion of 70.0%, making it the primary focus for carbon management of T&R equipment in an application-oriented university in China.
• Equipment (A02) contributes more carbon emissions than the other three categories across all three stages. Information technology equipment (A0201) contributes significantly to embodied carbon during both production and scrapping stages, while instruments and meters (A0210) and electrical/electronic production equipment (A0233) make notable contributions during the use stage. Therefore, emission reduction efforts should prioritize these subcategories.
• For most equipment, total carbon emissions can be reduced by about 233 tCO₂/a on average if retired one year in advance, whereas for information technology equipment (A0201), premature retirement may actually increase emissions (by approximately 48 tCO₂ per year). The results suggested that it is necessary to replace the T&R equipment with high energy consumption during the usage stage (e.g., A0210, A0233) on timely, but prolong the equipment with high embodied carbon content (e.g., A0201) to reduce the full lifecycle carbon footprint.
• Methodological Contributions and Applicability: In university settings, characterized by a wide variety of asset types and limited physical inventory data, the Expenditure-EEIO approach offers distinct advantages in terms of standardized measurement, replicability, scalability, and decision-making timeliness. It serves as a foundational tool for university Scope 3 management and asset renewal decisions, while also providing an actionable pathway for assessing the carbon performance of large-scale equipment renewal policies within the education system.

5. Limitations and Future Research

The limitations of this study are mainly reflected in the following three aspects: (1) the adopted EEIO database is the U.S. data (USEEIO v1.2), and there may be structural differences between the industry emission intensities and the actual situation in China, which may lead to a certain bias in the regional representativeness of the results of the study; (2) the ratio of the cost of disposal follows the empirical values of the subcategories directly, and fails to derive a more representative average value based on detailed statistical analyses of samples; (3) carbon accounting based on the expenditure method relies on the industry average emission factor, and it is difficult to accurately reflect individual differences in material composition and transportation routes of specific equipment.

Future research will focus on the following directions: (1) methodology optimization, building a database of EEIO emission factors applicable to Chinese universities to improve regional and industry representation; at the same time, integrating the process method with the expenditure method to establish a “hybrid LCA” analysis framework to take into account the availability of data and computational accuracy; in addition, through the establishment of a tracking system for the end-of-life flow of equipment to obtain the actual disposal cost and recycling data to further improve the accuracy of carbon accounting at the scrapping stage. (2) In terms of policy and decision-making support, carry out a multi-university comparative study to identify the carbon emission characteristics and management differences of T&R equipment in different types and regions of colleges and universities; construct a green procurement evaluation system for university equipment, and incorporate indicators such as carbon footprint, repairability, and the proportion of recycled materials used into the bidding evaluation criteria. (3) Integrated carbon–economic assessment, which will build upon the carbon footprint data established in this study. This involves conducting a comprehensive cost–benefit analysis of equipment management strategies (e.g., life-extension through refurbishment vs. replacement with energy-efficient models). The goal is to quantify the economic implications, such as net present value and payback periods, of various low-carbon transition pathways, providing a dual-perspective decision-support tool for university administrators.