Quality-Matched Life Cycle Assessment of CCU Supply Chains for SMR Tail Gas CO₂ in Industrial Parks

Abstract

Carbon capture and utilization (CCU) is imperative for industrial decarbonization. However, current life cycle assessment (LCA) methodologies often apply a static, one-size-fits-all approach, assuming a 99% CO₂ purity standard for all utilization pathways. This ignores the thermodynamic limits of capture technologies and the tolerance of certain endpoints for coarse gas, leading to severe over-purification energy penalties. To bridge this gap, we developed a quality-matched dynamic LCA framework targeting steam methane reforming (SMR) tail gas in industrial parks. A superstructure matrix was constructed, coupling 16 capture configurations (spanning chemical absorption to cryogenic separation across 85–99% purities) with five utilization pathways, under a dynamic grid decarbonization model (2024–2060). The baseline scenario shows that methanol is the most carbon-intensive pathway at 16.88 kg CO₂-eq per kg CO₂ utilized, whereas mineralization and concrete curing remain near break-even at 0.221 and 0.010 kg CO₂-eq, respectively. When low-purity demand is matched with PSA capture at 85–90% purity, the net GWP of mineralization and concrete curing decreases to 0.134 and 0.005 kg CO₂-eq, corresponding to capture-stage penalty reductions exceeding 60% relative to unnecessary 99% purification. Under the dynamic electricity scenario, concrete curing reaches the net-zero tipping point around 2031, and the coupled mineralization substitution strategy ultimately achieves −0.046 kg CO₂-eq per kg CO₂ utilized. These findings provide a compelling scientific basis for policymakers to design dual-grade CO₂ pipeline networks and prioritize low-purity, high-circularity building materials over carbon-intensive chemical synthesis in near-term industrial transitions.

1. Introduction

The transition toward carbon neutrality in heavy manufacturing sectors necessitates the extensive deployment of carbon capture and utilization technologies within integrated eco-industrial parks [1]. Steam methane reforming facilities represent a massive point source of greenhouse gas emissions while simultaneously offering a highly concentrated carbon dioxide stream ideal for downstream valorization [2]. However, incorporating these concentrated emission sources into regional supply chains presents significant environmental trade-offs because the capture and compression processes inherently consume substantial thermal and electrical energy [3]. When powered by fossil fuel dominated electricity grids, these energy intensive separation processes generate severe secondary emissions that can completely negate the primary climate mitigation benefits [4]. Consequently, determining whether these technological pathways deliver genuine environmental dividends depends heavily on the systemic optimization of the entire capture-to-utilization nexus rather than the isolated assessment of individual unit operations [5].

Recent CCU-LCA studies have substantially improved the evaluation of capture-to-product systems, yet three limitations remain prominent in the literature. First, many studies assess a single utilization pathway or a single capture technology, which obscures cross-matching opportunities within integrated industrial park supply chains [6,7]. Second, most studies adopt static background electricity assumptions and therefore cannot reveal the long-term tipping behavior induced by grid decarbonization [8,9]. Third, the majority of pathway comparisons implicitly treat 99% CO₂ as a universal feed standard, even though utilization endpoints differ markedly in impurity tolerance, reaction thermodynamics, and process safety requirements [10,11]. Accordingly, this work positions itself at the intersection of quality matching, dynamic LCA, and industrial park superstructure design, rather than as a conventional single-pathway CCU case study.

A critical research gap in current life cycle assessment methodologies is the rigid assumption of a universal high-purity standard for all captured carbon dioxide streams. Most existing techno-economic and environmental studies presume that captured gas must be purified to pipeline specifications exceeding 99% concentration regardless of its final industrial destination [6]. This one-size-fits-all paradigm fundamentally ignores the varying purity tolerances of different downstream utilization endpoints [11]. While fine chemical synthesis routes including methanol and urea production mandate ultra-high-purity feedstocks to prevent severe catalyst poisoning and ensure process safety, emerging construction material pathways possess entirely different thermodynamic requirements [12]. Technologies such as steel slag mineralization and fresh concrete curing can seamlessly sequester coarse gas mixtures because the carbonation reaction remains thermodynamically favorable even at lower partial pressures [7]. Forcing these low-purity tolerance pathways to consume highly refined carbon dioxide inflicts a severe over purification energy penalty driven by unnecessary vacuum compression and deep solvent regeneration [13]. Despite its critical importance, the environmental dividend of deploying quality-matched supply chains remains largely unquantified in the current sustainability literature.

Furthermore, traditional environmental assessments of these industrial networks suffer from static analytical biases that distort long term strategic planning. Conventional studies typically evaluate capture technologies under a fixed background energy system and fail to capture the profound impacts of regional grid decarbonization over extended temporal horizons spanning multiple decades [14]. This static approach significantly obscures the carbon lock in effects inherent to chemical synthesis pathways [15]. Specifically, processes relying on fossil based upstream feedstocks including gray hydrogen, ethylene oxide, and liquid ammonia will continue to generate massive indirect life cycle emissions even if the local electricity grid achieves absolute carbon neutrality [16]. Conversely, static assessments simultaneously underestimate the long term net negative emission potential of electrification driven physical capture processes when paired with permanent building material sinks [17]. A dynamic life cycle perspective spanning several decades is therefore needed to identify the exact temporal tipping points where specific utilization pathways transition from net emitters into authentic and verifiable carbon sinks [10].

To bridge these critical methodological gaps, this study develops a quality-matched dynamic life cycle assessment framework tailored for steam methane reforming tail gas utilization in heavy industrial parks [9]. By constructing a comprehensive superstructure matrix comprising four distinct capture technologies spanning chemical absorption to physical adsorption and cryogenic separation, this research systematically investigates the purity–energy response elasticity across various supply chain configurations [8]. The model links these diverse supply sources to five utilization endpoints across four distinct purity gradients ranging from 85% coarse gas to 99% refined gas [18]. To accurately reflect real-world engineering constraints, the framework integrates intermediate purification adapters that explicitly quantify the detour energy penalties and mass losses associated with upgrading low-quality gas streams for sensitive chemical applications [19]. Moreover, the model incorporates a dynamic temporal parameter simulating the aggressive decarbonization trajectory of the regional power grid from a coal intensive baseline in the current year to a highly renewable grid by the national carbon neutrality target year [20]. The analysis is organized around five core indicators introduced at the outset of the study: CO₂ purity requirement, capture energy demand, pathway-level GWP, net-zero tipping year, and the renewable share threshold required for net-negative performance. The remainder of this paper is structured as follows. Section 2 defines the goal and scope, inventory construction, database integration rules, and impact assessment method. Section 3 presents the technical matrix, purity classification logic, utilization pathways, and dynamic scenario assumptions. Section 4 discusses the baseline results, sensitivity analysis, pathway matching effects, and temporal transition behavior. Section 5 concludes with policy implications, study limitations, and future research directions.

2. Methodology

2.1. Goal and Scope Definition

The primary objective of this study is to quantify and compare the environmental performance of various CCU supply chain configurations, specifically focusing on the treatment of CO₂-rich tail gas from a SMR hydrogen production unit in an industrial park (Dalian, China). Unlike traditional life cycle assessment (LCA) studies that assume a uniform CO₂ purity of 99% [21], this research investigates the environmental trade-offs of matching different capture technologies with utilization pathways based on specific purity requirements.

Functional Unit (FU): The functional unit is defined as the treatment and utilization of 1 kg of CO₂ derived from SMR tail gas. This FU allows for a consistent comparison across different capture-to-utilization pathways [22].

System Boundary: A “cradle-to-gate” system boundary is adopted, as illustrated in Figure 1. The boundary encompasses (i) the foreground processes, including CO₂ capture from the SMR unit, purification adapters, and final utilization/mineralization processes, and (ii) the background processes, such as electricity generation, industrial steam production, and the upstream production of chemical co-reactants (e.g., ethylene oxide, ammonia). In accordance with ISO standards [23,24], the system boundary excludes the construction of capital equipment and infrastructure, as their environmental impact per unit of product is typically negligible in long-term industrial operations [25].

2.2. Life Cycle Inventory and Data Sources

To ensure the geographical representativeness and technical accuracy of the results, a hybrid data collection strategy was employed. Foreground Data: Technical parameters for the SMR unit and the CCU technologies were obtained from industry reports and detailed process simulations. The capture technology matrix includes four distinct routes (MEA, MDEA, PSA, and Cryogenic) across four purity gradients (85%, 90%, 95%, and 99%). Specific consumption factors for chemical feedstocks such as ethylene oxide (EO) and ammonia (NH₃) were sourced from the HiQLCD (High-Quality Life Cycle Database, China), which reflects the dominant coal-based production routes in the Chinese market [26]. Background Data: Generic background processes, including raw material extraction and transportation, were retrieved from the Ecoinvent 3.9.1 database [27]. Crucially, the electricity mix was localized by modifying the Ecoinvent datasets to represent the Northeast China Power Grid, accounting for the high share of coal-fired generation in the Dalian region. The integration rule between the two databases follows a foreground–background separation principle. Chinese pathway-specific industrial intermediates that strongly influence the GWP results, particularly EO, NH₃, and grid electricity, were preferentially represented with localized HiQLCD or regionally corrected datasets, whereas generic upstream processes such as transport, bulk material extraction, and auxiliary background services were retained from Ecoinvent 3.9.1. To avoid double counting, each foreground exchange was linked to only one background source after harmonizing units, lower heating values, and reference flows in OpenLCA. Regional correction was implemented by replacing the default electricity supplier of electricity-intensive unit processes with a Northeast China grid dataset calibrated to the scenario-specific emission factors described in Section 3.3. The major uncertainty sources therefore arise from database representativeness differences, regionalization of electricity, literature-derived capture parameters, and the temporal decarbonization scenario itself.

2.3. LCIA Method and Environmental Indicators

Life Cycle Impact Assessment (LCIA) was performed using the OpenLCA 2.5 software. Given the project’s focus on climate change mitigation and sustainability within industrial parks, the IPCC 2021 GWP100 method was selected as the primary characterization model. This method provides the most up-to-date characterization factors for greenhouse gas (GHG) emissions based on the IPCC 6th Assessment Report (AR6) (IPCC, 2021) [28]. The core indicator analyzed is the Global Warming Potential (GWP), measured in kg CO₂-equivalent (kg CO₂-eq). At the pathway comparison level, all results are reported per kg CO₂ utilized so that capture, upgrading, and utilization routes with different product yields remain directly comparable. To avoid burden shifting, results were cross-checked for internal consistency in energy and mass balances. For pathways involving multiple products (e.g., mineralization and curing), the system expansion method was applied to handle multifunctionality, as recommended by the Global CO₂ Initiative guidance and related TEA/LCA harmonization studies [29,30].

To convert the foreground inventory in

Table 1. Foreground life cycle inventory for CO₂ utilization pathways and purification adapters (per 1 kg of primary output).

Process Category	Material Inputs	Energy Demand	Outputs and Avoided Burdens
S1: Mineralization	CO2 (0.20 kg); Steel slag (0.80 kg)	Elec: 0.05 kWh	Aggregate (1.0 kg); Gravel (−1.0 kg)
S2: EC Synthesis	CO2 (0.50 kg); Ethylene oxide (0.50 kg)	Elec: 0.08 kWh; Heat: 0.80 MJ	Ethylene carbonate (1.0 kg)
S3: Methanol	CO2 (1.38 kg); Green H2 (0.19 kg)	Elec: 0.40 kWh; Heat: 4.00 MJ	Methanol (1.0 kg)
S4: Urea	CO2 (0.73 kg); Liquid ammonia (0.57 kg)	Elec: 0.14 kWh; Heat: 0.95 MJ	Urea (1.0 kg)
S5: Concrete Curing	CO2 (0.015 kg); Raw concrete (1.00 kg)	Elec: 0.005 kWh	Cured block (1.0 kg); Concrete (−1.0 kg)
Adapter: 95% → 99%	Coarse CO2 95% (1.06 kg)	Elec: 0.05 kWh	Refined CO2 99% (1.0 kg)
Adapter: 90% → 99%	Coarse CO2 90% (1.15 kg)	Elec: 0.12 kWh	Refined CO2 99% (1.0 kg)
Adapter: 85% → 99%	Coarse CO2 85% (1.24 kg)	Elec: 0.20 kWh	Refined CO2 99% (1.0 kg)

from “per 1 kg of primary output” to the common comparison basis of “per 1 kg CO₂ utilized,” each pathway result was divided by its pathway-specific CO₂ uptake coefficient. For example, methanol uses 1.38 kg CO₂ per kg product, so the product-based inventory was normalized by 1.38 to obtain the result per kg CO₂ utilized; urea, EC synthesis, mineralization, and concrete curing were treated analogously using coefficients of 0.73, 0.50, 0.20, and 0.015 kg CO₂ per kg product, respectively.

3. Scenario Design and Key Assumptions

3.1. Technical Matrix of Capture Technologies

To evaluate the “purity–energy trade-off” across the CCU supply chain, a comprehensive technical matrix comprising four capture technologies (MEA, MDEA, PSA, and Cryogenic separation) and four target CO₂ purities (85%, 90%, 95%, and 99%) was established. The core assumption rests on the differing energy response behaviors of these technologies when purity requirements are relaxed.

Chemical absorption methods (MEA and MDEA) are modeled as “purity-rigid” technologies. Their energy consumption is dominated by the sensible heat needed to raise the solvent temperature and the desorption duty required to regenerate the solvent and strip dissolved CO₂ [31]. Because these thermal loads are largely governed by solvent circulation and phase-equilibrium constraints rather than by small changes in product purity, lowering the target purity from 99% to 85% only weakly reduces the specific regeneration duty. Consequently, relaxing the target purity from 99% to 85% yields only limited thermal energy savings and does not fundamentally change the capture burden. Conversely, PSA is modeled as a “purity-elastic” technology. As a physical separation process driven by adsorption equilibrium, pressure swing, vacuum generation, and product recompression, reducing the target purity allows higher recovery at lower desorption severity and significantly minimizes specific power consumption [32]. Cryogenic separation is treated as a high-purity-oriented route because its deep cooling and phase-change duty becomes more competitive only when high CO₂ concentrations and stringent purity requirements justify the refrigeration penalty; it is therefore classified as comparatively rigid on the low-purity side even though its electricity demand still decreases monotonically with relaxed purity. The values in

Table 2. Energy consumption matrix of carbon capture technologies across different CO₂ purity gradients.

Capture Technology	Purity Level	Electricity Demand (kWh/kg CO2)	Thermal Demand (MJ/kg CO2)	Technology Type
A0: MEA	99% (Industrial grade)	0.32	3.50	Purity-Rigid
	95% (Fine grade)	0.30	3.20
	90% (Coarse grade)	0.28	2.90
	85% (Raw grade)	0.26	2.60
A1: MDEA	99% (Industrial grade)	0.22	2.50	Purity-Rigid
	95% (Fine grade)	0.20	2.30
	90% (Coarse grade)	0.18	2.10
	85% (Raw grade)	0.16	1.90
A2: PSA	99% (Industrial grade)	0.55	0.00	Purity-Elastic
	95% (Fine grade)	0.40	0.00
	90% (Coarse grade)	0.30	0.00
	85% (Raw grade)	0.22	0.00
A3: Cryogenic	99% (Industrial grade)	0.75	0.50	High-End/Rigid
	95% (Fine grade)	0.65	0.50
	90% (Coarse grade)	0.55	0.50
	85% (Raw grade)	0.45	0.50

should thus be interpreted as literature-calibrated scenario parameters that capture the first-order thermodynamic response of each technology family, rather than as universally fixed plant constants.

3.2. CO₂ Utilization Pathways

The demand side of the CCU network evaluates five utilization pathways, categorized by their tolerance for CO₂ impurities:

High-purity pathways (required purity ≥ 99%): (1) S2 (Ethylene Carbonate Synthesis): Synthesized via the cycloaddition of CO₂ and EO. The high carbon footprint of fossil-based EO is explicitly included; (2) S3 (Methanol Synthesis): Modeled via CO₂ hydrogenation (${\mathrm{CO}}_2+3{\mathrm{H}}_2\to {\mathrm{CH}}_3\mathrm{OH}+{\mathrm{H}}_2\mathrm{O}$). To accurately reflect future decarbonization potential, the hydrogen feedstock is assumed to be green hydrogen produced via Proton Exchange Membrane (PEM) water electrolysis; (3) S4 (Urea Synthesis): A mature industrial benchmark pathway reacting high-purity CO₂ with liquid ammonia (NH₃).

Low-purity pathways (tolerated purity 85–90%): (1) S1 (Steel Slag Mineralization): CO₂ reacts with calcium/magnesium silicates in steel slag. Utilizing the system expansion approach, the produced carbonated aggregate is assumed to substitute natural crushed gravel on a 1:1 mass basis, generating an avoided environmental burden [33]; (2) S5 (Concrete Curing): CO₂ is injected into fresh concrete mixtures, forming nano-scale calcium carbonate that enhances early-stage compressive strength [34]. Through system expansion, the cured concrete blocks substitute ordinary concrete blocks, implicitly accounting for the reduction in carbon-intensive cement usage required to achieve equivalent structural performance. For consistency, this study distinguishes between the conservative base-case purity requirement and the feasible lower-bound purity. S1 is modeled as directly compatible with 85% CO₂, whereas S5 is modeled with a conservative direct-supply requirement of 90% CO₂ in the pathway ranking analysis, while 85% is retained as the lower-bound sensitivity case to represent flue gas-tolerant curing conditions reported in the literature.

3.3. Integrative Supply Chain Decarbonization Pathways

To facilitate cross-matching between low-purity capture streams and high-purity utilization endpoints, intermediate “purification adapters” were modeled. If a downstream chemical plant utilizes 85% coarse CO₂ captured via PSA, the model automatically integrates an adapter process that upgrades the gas to 99%. This step introduces a calculated “detour penalty,” accounting for secondary compression electricity and a base-case 6% pure CO₂ loss during refinement. This value was adopted as a conservative engineering allowance for multistage conditioning and recompression during upgrading, consistent with the non-negligible auxiliary losses discussed for integrated CO₂ processing chains [13,19]. To test robustness, the adapter parameter was also checked over a 3–10% range, and the qualitative ranking of direct high-purity supply versus staged purification remained unchanged because the electricity burden, rather than the mass-loss term alone, dominates the detour penalty under a coal-intensive grid.

Furthermore, a dynamic temporal model spanning from 2024 to 2060 was designed to capture the impact of the energy transition. Based on China’s carbon neutrality planning, the proportion of renewable energy such as wind and solar is projected to increase progressively from a scenario baseline of 0% renewable share in 2024, which is used here as a stylized representation of a coal-dominated electricity supply rather than as a literal statistical description of the actual Northeast China grid, to 40% by 2030, ultimately reaching 95% by 2060 [35]. The annual grid emission factor was interpolated between these milestone years and applied as an average grid emission factor to all electricity-consuming foreground processes. This average-factor choice is appropriate for long-horizon comparative pathway assessment, whereas marginal factors were not used and are treated as a limitation for future refinement. This temporal parameter dynamically alters the life cycle inventory of all electricity-driven foreground processes, especially PSA capture and PEM electrolysis, allowing for the identification of specific tipping points where CCU technologies transition into net-negative emission status [36].

4. Results and Discussion

4.1. Baseline Performance Hotspot Identification

The life cycle GWP of the five utilization pathways under the 2024 baseline scenario, which assumes a coal-dominated grid and utilizes traditional MEA capture at 99% purity, is presented in Figure 2. The results reveal a massive environmental disparity between chemical synthesis routes and construction material routes.

The chemical pathways exhibit significantly high carbon footprints, with methanol (S3), ethylene carbonate (EC) synthesis (S2), and urea (S4) generating 16.88, 3.09, and 2.75 kg CO₂-eq/kg CO₂ utilized, respectively. A contribution analysis indicates that the capture process itself accounts for a minor fraction of the total impacts, such as 6.9% for S3 and 13.7% for S2. Instead, the system is severely burdened by the upstream production of chemical co-reactants—a phenomenon identified as the “carbon lock-in effect” [37]. For instance, in the S3 pathway, the provision of green hydrogen via PEM electrolysis consumes approximately 55 kWh/kg H₂. Under the 2024 Northeast China grid mix of high carbon intensity, this electricity demand translates to a staggering 14.89 kg CO₂-eq, completely negating the climate benefits of utilizing the captured CO₂. Similarly, the fossil-based production of ethylene oxide and liquid ammonia dominates the environmental burdens of S2 and S4.

Conversely, the low-purity tolerance pathways, mineralization (S1) and concrete curing (S5) demonstrate superior environmental performance with net GWPs of 0.221 and 0.010 kg CO₂-eq, respectively. The incorporation of system expansion successfully captures the avoided burdens from substituting natural gravel (−0.018 kg CO₂-eq) and ordinary concrete blocks (−0.007 kg CO₂-eq) [38]. Nevertheless, under the current energy structure, these pathways remain net-positive emitters, fundamentally constrained by the rigid energy penalty of the MEA capture unit.

4.2. Purity–Sensitivity Energy Efficiency Impacts

To overcome the capture energy bottleneck observed in the baseline, the sensitivity of the system’s GWP to CO₂ purity requirements was investigated, as illustrated in Figure 3. The analysis compares a purity-rigid technology, namely MEA, against a purity-elastic technology, namely PSA, across a purity spectrum from 99% down to 85%.

As depicted in Figure 3a,b, the MEA-Net curves remain relatively flat. Reducing the purity target to 85% only yields a marginal GWP reduction of 6.5% for pathway S1 and 10.5% for pathway S5. This confirms that chemical absorption is thermodynamically constrained by the latent heat of water vaporization during solvent regeneration. In stark contrast, the PSA-Net curves exhibit a precipitous drop. By aligning the capture purity with the direct-demand window of low-purity pathways, namely 85% for mineralization and a conservative 90% with an 85% lower-bound sensitivity for concrete curing, the specific power consumption of the vacuum compressors is drastically reduced. This quality-matching strategy drives the net GWP of S1 down to 0.134 kg CO₂-eq, representing a 39.1% improvement over the baseline. Simultaneously, the low-purity curing window reduces S5 to 0.005–0.006 kg CO₂-eq, with the 85% sensitivity case producing the lower bound and confirming that the conclusion is not sensitive to whether the conservative direct-supply threshold is set at 90% or the feasible lower-bound threshold is set at 85%.

The shaded areas between the PSA-Net and MEA-Net curves conceptualize the Quality-Matching Dividend. This dividend expands significantly at lower purity thresholds, proving that over-purifying CO₂ for construction materials is a major source of resource inefficiency in current industrial parks. The comprehensive supply–demand matrix in Figure 4 further visualizes this paradigm. MDEA emerges as the optimal choice for high-purity chemical endpoints including S2, S3, and S4 due to its lower regenerative heat duty compared to MEA. Conversely, employing physical adsorption or cryogenic technologies for these 99% purity demands triggers severe electricity penalties driven by intensive vacuum compression and deep refrigeration. Such massive electrical burdens render them environmentally inferior to chemical absorption under the current fossil-based grid. Meanwhile, PSA dominates the low-purity sectors such as mineralization pathway S1 and concrete curing pathway S5, establishing a distinct diagonal optimization trajectory across the CCU superstructure.

4.3. Optimal Pathway Supply Chain Synergy

While quality matching is highly effective for low-purity applications, high-purity endpoints like methanol synthesis pathway S3 face a different optimization challenge. Figure 5 contrasts the environmental impacts of direct high-purity supply strategies versus a detour strategy, wherein 85% coarse CO₂ from PSA is upgraded to 99% via an intermediate purification adapter.

Under the 2024 carbon-intensive grid, the direct MDEA supply route achieves the lowest GWP at 16.53 kg CO₂-eq. In contrast, the detour strategy involving 85% coarse CO₂ capture via PSA followed by an intermediate purification adapter unexpectedly underperforms by registering the highest GWP at 17.24 kg CO₂-eq. Although the initial PSA step operates at a highly efficient 0.22 kWh/kg, the subsequent refinement requires an additional 0.20 kWh/kg for secondary compression alongside a 6% mass loss. In a coal-dominated grid, this electricity penalty heavily outweighs the primary capture savings. This finding provides a crucial engineering guideline indicating that in regions lacking abundant renewable energy, staged purification creates adverse environmental trade-offs. Therefore, integrated and single-step advanced chemical absorption using technologies like MDEA remains the Best Available Technology (BAT) for chemical synthesis pathways in the near term.

4.4. Future Evolution Decision-Making Matrix

Recognizing that CCU infrastructure operates over decades, a dynamic LCA was conducted to project the environmental trajectories of the pathways in response to China’s grid decarbonization targets spanning from 2024 to 2060.

As depicted in Figure 6b, the EC synthesis pathway S2 and urea production pathway S4 display nearly flat temporal curves. Despite the renewable energy share reaching 95% by 2060, their net GWPs merely decrease to 2.74 and 2.71 kg CO₂-eq respectively. This persistent carbon lock-in reinforces that electrifying the capture process is futile for these pathways unless their fossil-based co-reactants are fully substituted with bio-based or circular alternatives [39]. Conversely, the methanol pathway S3 exhibits a radical transformation, plummeting from the highest emitter in 2024 to a highly competitive 1.56 kg CO₂-eq by 2060, unlocking its potential as a sustainable energy carrier.

Most notably, Figure 6c identifies critical net-zero tipping points. Driven by the purity-elastic PSA capture and increasing grid cleanliness, the concrete curing pathway S5 enters the carbon-negative zone in the early 2030s. The mineralization pathway S1, burdened by slightly higher grinding energy, successfully crosses the net-zero threshold around 2053. The paradigm shift illustrated in Figure 6d demonstrates that long-term CCU investments in industrial parks must pivot from solely focusing on high-value chemicals towards integrating high-circularity and low-purity construction materials, which serve as authentic and permanent carbon sinks. From an engineering perspective, this conclusion also supports a dual-grade CO₂ logistics concept. In eco-industrial parks where transport distances are short and emitters and users are colocated, segregating coarse-gas and refined-gas delivery can be technically feasible because it reduces unnecessary polishing at the source and reserves deep purification only for chemical users. Although the present study does not include a full techno-economic analysis of pipeline diameters, compression stations, or dispatch control, the LCA results indicate that such differentiated infrastructure is environmentally justifiable and should be prioritized for further engineering design.

The exact mathematical tipping points and lock-in vulnerabilities are precisely extracted and summarized in

Table 3. Decarbonization thresholds, net-zero tipping points, and lock-in vulnerabilities of the optimal CCU configurations.

Utilization Pathway	Optimal Capture Strategy	Net-Zero Tipping Year	Renewable Threshold	2060 GWP Reduction	Lock-in Vulnerability
S5: Curing	PSA (90% direct)	∼2031	41.7%	>100% (Net-neg.)	Low (Mineral sink)
S1: Mineral.	PSA (85% direct)	∼2053	88.4%	>100% (Net-neg.)	Low (Mineral sink)
S3: Methanol	MDEA (99% direct)	N/A	N/A	90.7%	Low (Green H2)
S2: EC Syn.	MDEA (99% direct)	N/A	N/A	11.3%	High (Fossil EO)
S4: Urea	MDEA (99% direct)	N/A	N/A	1.5%	High (Fossil NH3)

. This comprehensive matrix delineates the exact mathematical thresholds where quality-matched mineral sinks transition into net-negative technologies driven by specific regional renewable energy penetration targets. In stark contrast, the matrix highlights that conventional chemical synthesis routes exhibit severe carbon lock-in vulnerabilities and fail to achieve absolute carbon neutrality due to their persistent reliance on carbon-intensive upstream feedstocks.

To synthesize the optimization strategies, a step-by-step mitigation pathway for the mineralization route S1 is delineated in Figure 7a. The baseline configuration utilizing MEA capture at 99% purity generates a cumulative GWP of 0.231 kg CO₂-eq. Implementing the quality-matching strategy by shifting to PSA capture at 85% purity reduces this footprint to 0.134 kg CO₂-eq. Incorporating the projected 50% grid decarbonization for 2030 further lowers the emissions to 0.084 kg CO₂-eq. The ultimate breakthrough is achieved through a product upgrade where the mineralized aggregate substitutes high-carbon concrete blocks rather than low-value gravel. This profound circularity substitution completely offsets the remaining emissions and drives the system into a net-negative status at −0.046 kg CO₂-eq. Such a cascading reduction proves that deep industrial decarbonization requires synergistic interventions across capture technologies, energy transition, and product displacement dimensions.

To translate these life cycle findings into actionable industrial park management, an eco-efficiency quadrant matrix is proposed in Figure 7b. This matrix categorizes the five utilization pathways based on their minimum achievable net GWP and required CO₂ purity, with the size of each scatter point representing the magnitude of its circularity potential via avoided burdens. The low-purity and low-emission region designated as Quadrant III emerges as the immediate action zone. Both the mineralization pathway S1 and the concrete curing pathway S5 reside in this optimal zone, exhibiting robust carbon reduction capabilities and substantial circularity potential. Conversely, the chemical synthesis routes including methanol, ethylene carbonate, and urea fall into the high-purity and high-emission region designated as Quadrant II. This policy restriction zone highlights that without radical upstream feedstock decarbonization, funneling captured CO₂ into fine chemicals provides negative environmental dividends. These multidimensional evaluations offer a robust scientific basis for prioritizing infrastructure investments such as dual-grade CO₂ pipeline networks in eco-industrial parks.

4.5. Study Limitations and Future Research

Several limitations should be acknowledged when interpreting the present results. First, the foreground–background integration combines HiQLCD and Ecoinvent 3.9.1, and although harmonization and regional replacement rules were applied, residual uncertainty remains in the representativeness of upstream industrial intermediates and auxiliary services. Second, the capture energy matrix and purification-adapter parameters were derived from literature-calibrated engineering values rather than plant-specific operating data, which introduces parameter uncertainty into the magnitude of the Quality-Matching Dividend. Third, the dynamic electricity scenario uses average grid emission factors and milestone interpolation, so it is more suitable for strategic pathway comparison than for marginal dispatch analysis. Fourth, capital construction, detailed pipeline economics, and operational control of multi-user CO₂ networks were excluded from the present cradle-to-gate boundary.

Future work should therefore focus on four directions: site-specific industrial data collection for SMR tail-gas capture systems, explicit uncertainty propagation for purity–energy response parameters, coupled LCA-TEA optimization of dual-grade CO₂ transport infrastructure, and dynamic modeling with marginal electricity factors and additional utilization pathways. These extensions would strengthen the decision relevance of quality-matched CCU planning for real industrial parks.

5. Conclusions and Policy Implications

This study developed a quality-matched dynamic life cycle assessment framework to evaluate carbon capture and utilization supply chains for steam methane reforming tail gas in industrial parks. The revised results confirm three main conclusions. First, chemical synthesis pathways including methanol, ethylene carbonate, and urea remain vulnerable to upstream carbon lock-in under a coal-dominated grid, with methanol reaching 16.88 kg CO₂-eq per kg CO₂ utilized in the baseline scenario. Second, matching low-purity-tolerant applications with PSA capture at 85–90% purity substantially reduces unnecessary polishing energy, lowering mineralization and concrete curing pathways to 0.134 and 0.005–0.006 kg CO₂-eq, respectively. Third, under progressive grid decarbonization, the concrete curing route crosses its net-zero tipping point around 2031, whereas the coupled mineralization substitution strategy can ultimately reach −0.046 kg CO₂-eq per kg CO₂ utilized. Meanwhile, advanced chemical absorption utilizing methyldiethanolamine remains the optimal single-step capture technology for high-purity chemical endpoints, as staged purification detour strategies incur excessive electricity penalties.

These findings support three immediate policy implications. Eco-industrial parks should prioritize differentiated CO₂ management infrastructure so that coarse gas can be supplied directly to construction-material sinks while refined gas is reserved for high-purity chemical users. Industrial standards should move from a universal purity specification toward fit-for-purpose quality matching, especially for colocated supply chains in which transport distances are short and over-purification can be avoided. In parallel, high-purity chemical CCU projects should be evaluated together with green-hydrogen and low-carbon-feedstock availability; otherwise, the apparent utilization benefit may simply shift emissions upstream. At the same time, the conclusions should be interpreted together with the study limitations discussed above, particularly the use of literature-derived capture parameters, average grid emission factors, and the exclusion of detailed pipeline economics.