The potential role of truck-hailing and operational efficiency improvement in China’s road freight decarbonization

Domain: biology
Published: 2026-04-02
Editorial outcome: major_revision

Abstract
Truck-hailing is an emerging business model that could potentially transform the road freight sector. As a new form of
horizontal collaborative transport, it offers great potential to improve operational efficiency—an important yet
underrepresented demand-side strategy in current transport decarbonization research. Drawing on a proprietary national
truck-hailing dataset (N=51,021) and an enhanced vehicle fleet model that incorporates operational factors, here we assess the
potential carbon emission impacts of truck-hailing and operational efficiency improvements in China under multiple scenarios.
Under medium projections, enhancements in load factor and reductions in empty running could cut China’s road freight
emissions by 886-2203 Mt (9%-24%) in the near term (2020–2035) and 2504-3327 Mt (23%-31%) in the long term (2035–2060),
compared to a business-as-usual pathway. However, capacity constraints of zero-emission vehicles may reduce these benefits,
resulting in roughly 3% higher emissions in the near term and nearly 10% in the long term.

Introduction
Truck-hailing, or digital freight matching, is an emerging business model that could potentially transform the road freight
sector1-6. As part of the broader digitalization and sharing economy megatrends7, this cloud-based logistics service primarily
caters to the spot (non-contractual) road freight market by matching shipper demand with carrier supply via mobile apps8-10. In
addition to load matching, truck-hailing platforms often feature route optimization and real-time load tracking, similar to a fleet
or transportation management system11(Fig. 1). Since its inception, truck-hailing has gained rapid market traction across
developed economies, including the United States and Europe8,10. In China—the world’s largest road freight market by tonne-
kilometer—it has also experienced strong growth since its emergence in 201312, reaching an estimated 22% penetration at
present (methods). According to the latest statistics, there were over 130 million truck-hailing journeys in 2023, fulfilled by
more than 7.99 million trucks—equivalent to almost 70% of China’s commercial trucks fleet4 (Supplementary Fig. 1).

Globally, road freight transport is a major driver of rising energy consumption and carbon emissions, fueled by rapidly
expanding road freight activities that are projected to more than double by 20501,13. Trucking could potentially surpass
passenger vehicles as the largest oil consuming sector, as demand growth offsets anticipated efficiency gains while large-scale
deployment of low-carbon technologies remains in its early stages2,14. Although most existing decarbonization strategies in this
sector have focused on enhancing vehicle efficiency and adopting low-carbon fuels, there has been comparatively limited
attention to mitigating emissions from a logistics or service delivery perspective 2,15. Notably, Chapter 5 of the Sixth IPCC
Assessment Report highlights service delivery efficiency improvements at the end-use phase as a critical demand-side
mitigation strategy for transport and other sectors7. According to the International Transport Forum, logistics and supply chain
optimization could potentially deliver 30-50% reductions in road freight emissions2, largely through operational efficiency
improvements—such as increased vehicle utilization—enabled by new information and communication technologies1-2,15-16.

Leveraging the latest mobile internet technologies, truck-hailing essentially represents a new iteration of horizontal
collaborative transport solutions5,8, which is recognized as one of the most effective methods for enhancing operational
efficiency and sustainability in freight transport9,17-20. Horizontal collaboration refers to cooperation among logistics players at
the same level of the supply chain, and has been extensively studied in operational research and logistics literature9,20-21.
Compared to conventional non-collaborative transport, existing research indicates that horizontal collaborative solutions have
the potential to substantially improve trucking efficiency—especially for small carriers—by reducing empty hauling, improving
load factor (or payload capacity utilization), cutting down unnecessary travel, and decreasing fleet size via asset sharing and
route optimization6,8-9,18-19,21-35. Likewise, recent research on China’s emerging digital freight platforms has found that, relative
to traditional analogue and fragmented (non-collaborative) freight forwarding business model, truck-hailing services facilitate
better load-matching and boost revenue-generating mileage for carriers by expediting turnaround and minimizing empty
journeys12,36 (Supplementary note 1).

While most existing studies have investigated the efficiency impact of horizontal collaborative transport and digital
freight matching using small samples or simulations at the micro level, there remains a gap in understanding how increasing
digitalization of road freight—and operational efficiency gains of trucking as a potential result—could shape sectoral carbon
emissions and impact national decarbonization pathways. Among the existing research is this aspect, McKinnon (2018) notes
that reducing empty running, improving load factor, and utilizing high-capacity vehicles each contributes to lowering road
freight emissions15. Mulholland et al. (2018) estimate that combined measures of road freight operational and logistics
improvements could lead to around 30% cumulative GHG emissions reduction in 2050 compared to the reference case37.
Khanna et al. (2021) and Wang et al. (2022) identify freight vehicle operational and logistics improvement as a viable strategy to
decarbonize China’s HDV sector38-39, while Xue and Liu (2022) highlight that increases in vehicle occupancy (average load per
truck) offer high potential for near-term decarbonization in China’s road freight sector40. Nevertheless, many of these studies
have based their analyses on assumed logistics improvements, underscoring the need for more robust empirical evidence.
Moreover, existing research is limited by methodological gaps that precludes a comprehensive account of important
operational efficiency factors.

In this study, we begin to address these research gaps, utilizing recent empirical data and an enhanced modeling
approach to explore the potential macro-level emission implications of truck-hailing and operational efficiency improvements.
We draw on a high-resolution national truck-hailing sample from China, featuring predominantly long-haul journeys undertaken
by medium- and heavy-duty trucks (MHDTs). China is the world's second largest road freight CO2 emitting country, with its
freight vehicles accounting for 25% of global road freight emission growth between 2000 and 20151. However, despite China’s
rapidly expanding road freight demand, operational efficiency in its trucking sector remains relatively low compared to that of
many developed economies (Supplementary Table 1, Supplementary Fig. 11 and Supplementary note 1). As the Chinese
government pledges to strengthen the logistics system as a key strategy to achieve peak economy-wide carbon emissions
before 203041, it is crucial to understand how truck-hailing and operational efficiency improvements can help advance
decarbonization in this sector, given the rapid uptake of road freight digitalization.

We analyze several key operational efficiency indicators—namely vehicle empty running, load factor, vehicle
kilometers travelled (VKT)—which are key determinants of freight transport energy consumption and carbon emissions. While
most freight transport models in existing transport decarbonization research and Integrated Assessment Modeling frameworks
do not explicitly account for all of these parameters, here we developed an enhanced vehicle fleet model to incorporate these
factors, and utilized multiple scenarios to explore how different trajectories of digital freight penetration and operational
efficiency gains at the sectoral level may shape the overall road freight decarbonization pathway. Meanwhile, as China
accelerates the adoption of zero-emission vehicles (ZEVs) to curb sector emissions41, we examine a critical yet often overlooked
issue—the impact of ZEV operational limitations on decarbonization pathways that prioritize large-scale ZEV deployment.
Specifically, we assess how constraints in ZEV performance—such as reduced payload capacity and driving range, increasingly
highlighted in recent studies42-48—may diminish their potential emission reduction benefits.

Our analysis, comparing against a baseline scenario, reveals great potential for reducing China's road freight
emissions through a combination of logistics optimization and low-carbon technology adoption. Under medium projections,
operational and logistical enhancements could yield cumulative emission reductions of 886-2203 MT (9%-24%) from 2020 to
2035 (near term) and 2504-3327 MT (23%-31%) from 2035 to 2060 (long term). Optimistic scenarios project even greater
reductions, reaching 1880-3014 MT (20%-32%) and 3505-3543 MT (32%-33%) for the respective periods. These findings
highlight a synergistic relationship between logistics optimization and technological advancements across both timeframes.
Improved logistics offers near-term decarbonization opportunities, and can also lessen the long-term investment for low-
carbon technologies and infrastructure by minimizing unnecessary travel. Our results indicate that combined operational and
technological measures could decrease the total vehicle stock by over two-thirds by 2060, compared to relying solely on low-
carbon technologies. Meanwhile, the operational limitations of ZEVs, particularly in payload capacity, are projected to have
negative emission impacts of approximately 3% in the near-term and nearly 10% in the long-term.

Results
Truck-hailing services have achieved rapid development in China’s long-haul and urban freight markets during recent years,
primarily serving owner-operators who constitute the vast majority of road freight carriers and HDT stock in the country
(Supplementary Note 1). Employment surveys conducted by the China Federation of Logistics & Purchasing indicate that online
freight matching has gradually surpassed traditional offline freight brokers as a more important load source for truck drivers in
China in the last few years49-51. The proprietary truck-hailing sample analyzed in this study focuses on the long-haul freight
sector, encompassing 51,021 digitally matched, single origin-destination journeys completed mainly by MHDTs in late 2018.
These journeys span all administrative regions (provinces and municipalities) and public road categories in mainland China
(Supplementary Fig. 2). All carriers in the sample are full-time owner-operators who exclusively source their loads from freight-
matching platforms during the sample period. The dataset integrates waybill, geospatial, and vehicle operation data from
freight journeys. This synthesis allows for the calculation of key operational performance metrics and provides the foundation
for subsequent in-depth modeling analysis (Methods).

Fig. 2 presents the patterns of load-to-vehicle and journey-to-vehicle matching within the sample. Overall, there is a
relatively good fit between cargo weight and vehicle capacity (Fig. 2a). As cargo weight increases, vehicles with larger payload
capacity, especially tractor-trailers, are more frequently utilized for delivery. The proximity of the medium values to the 45-
degree dashed line suggests that online matching has enabled many vehicles to achieve near-full capacity utilization. However,
the figure also suggests that overloading remains common in China, due to low barriers to freight market entry, intense
competition among owner-operators that account for majority of the MHDT carriers and vehicle stock, and the resulting low
profit margins11,52-53.

Regarding journey-to-vehicle matching, there are not many visible differences in usage profiles across vehicles with
varying payload capacities (Fig. 2b). Smaller capacity vehicles, such as medium freight trucks (MFTs), are extensively used for
long-distance inter-city and inter-province journeys. This is notable given that heavy freight trucks (HFTs), such as tractor-
trailers, are generally considered more energy-efficient for long-haul applications due to their large payload capacity54. This
may reflect owner-operators’ limited ability to invest in the most energy-efficient vehicles for cargo delivery, as well as
shippers’ challenges in consolidating loads to maximize vehicle capacity utilization.

Table 1 shows the proportion of vehicle empty running mileages for both intra- and inter-province journeys within the
sample. So far, research on truck travel behavior concerning empty mileage in China remains very limited. One commonly cited
figure places this at approximately 40%11,52,40,59. For comparative purposes, we also present estimates derived from China’s
2014 tolled expressway statistics60 (see Supplementary Note 2 for more discussion on the comparison). Two observations
emerge from Table 1. First, inter-province trips consistently exhibit lower percentages of empty miles compared to intra-
province journeys. This disparity is likely due to the high opportunity costs associated with empty running during long-distance
multi-province travel. Second, while the expressway data indicates that HFTs have less empty running than MFTs, the truck-
hailing sample shows virtually no differences between these vehicle groups. This could be attributed to route optimization
inherent in truck-hailing services.

Overall, the sample results reveal substantially lower levels of empty running than the expressway data. The empty
running ratio observed in our sample approaches the efficiency levels reported in the China Federation of Logistics &
Purchasing’s 2021 survey of key commercial fleet, which record approximately 10% of empty running61. Our estimates are also
closely aligned with figured reported by several EU countries—such as Portugal, Finland, Spain, Hungary, Greece, Denmark, and
Belgium—where empty running ratios for comparable international journeys range from 6.5% to 10.8%, and with the EU-27
average of 12.2% in 201862. Meanwhile, the tolled expressway statistics may underestimate the actual share of vehicle empty
miles for several reasons. Primarily, they do not fully capture the entirety of trips, excluding segments such as the first- and last-
mile that occur off tolled roads. Additionally, China’s high toll charges make empty running on expressways prohibitively
expensive52. In fact, our sample indicates truck drivers frequently opt for toll-free national and provincial highways, even when
expressways offer much shorter routes. Furthermore, the expressway statistics may predominantly reflect the travel behaviors
of large commercial fleets or those engaged in contract logistics, which typically face stricter time constraints and have
healthier profit margins, as owner-operators are often deterred from using toll roads by high costs50. Recent GPS data from
millions of commercial trucks indicate that, in 2021, the average percentage of empty running was 34%-36% for straight and
dump trucks and 27% for tractor-trailers in China51. Hence, the actual share of truck empty mileage in China may still hover
around 30% or higher.

Table 2 presents the vehicle load factor observed within our sample. Data regarding China’s truck load factor are
scarce. For comparison analysis, we include estimates derived from mean vehicle loads reported for various vehicle types in
China’s 2010 tolled expressway statistics, the last year such data was publicly available63. In the truck-hailing sample, the load
factor for MFTs and HFTs are remarkably similar, both approaching 90%. This consistency reflects the efficient load-vehicle
matching process. On the other hand, while HFTs exhibit comparable load factor across both datasets, MFTs again
underperform HFTs in the expressway data. It is important to note, however, that the load factor values extrapolated from
expressway statistics may overestimate average utilization and again are more likely to reflect the capacity utilization among
vehicles engaged in contract logistics. This potential overestimation stems from the same factor that discourages empty
running: high toll charges disincentivize inefficient use of vehicle capacity and discourage low-margin trips by owner-operators.

Next, we examine daily VKT levels within our sample and compare them to various external sources. The VKT values
presented in Table 3 exhibit a wide range. Notably, the estimates derived from the truck-hailing sample align most closely with
those from the 2017 truck-hailing pilot project, but considerably exceed values reported by all other sources. It is important to
note that the comparative external data sources provide average VKT values that do not distinguish between large and small
carriers. In contrast, our truck-hailing sample specifically focuses on small carriers, particularly owner-operators, which
introduces a potential disparity in direct comparison. Logistics service provider G7, based on digital travel records from over
half a million trucks in China, reports average daily VKT of 500km for large commercial carriers (fleets of 5-500 vehicles) and
approximately 250km for small carriers/owner operators (fleets of fewer than 5 vehicles)64. These G7 figures correspond
reasonably well with the daily VKT estimates shown in Table 3, suggesting that the mileage performance of owner-operators in
our truck-hailing sample approaches that of large commercial fleets. Meanwhile, other reported values, such as those by
Ministry of Transport (MOT) and the China Automotive Technology and Research Center (CATARC), may more accurately
represent the daily mileage of small carriers/owner operators who primarily rely on traditional load-searching methods.

In summary, we observed substantially higher operational efficiency in the truck-hailing sample, aligning with findings
reported in the existing literature. Given that improvements in operational efficiency can directly and indirectly reduce energy
consumption and carbon emissions, their adoption at the road freight systemic level holds substantial implications for the
sector’s decarbonization pathway, should truck-hailing services maintain its current growth trajectory and achieve greater
market penetration. Therefore, in the following sections, we model the future development of truck-hailing services and the
operational efficiencies of MHDTs. We then explore the resulting implications for China’s road freight transport decarbonization
pathway. To accomplish this, we utilize the findings from our sample analysis and employ an enhanced vehicle fleet model that
incorporates key operational efficiency factors, including empty running, load factor, payload capacity and VKT (methods).

Cross-cutting scenarios for logistics & technological improvement
To explore future possibilities for truck-hailing adoption and operational efficiency development in China’s MHDT sector,
we constructed five scenarios. The first, a business-as-usual (BaU) scenario, maintains the current truck-hailing penetration rate.
The second and third scenarios present “optimistic” projections, anticipating rapid growth of China’s truck-hailing market
coupled with consistent improvements in average truck operational efficiency. These scenarios model freight digitalization
following a linear and an s-shaped growth pattern, respectively, both reaching defined saturation levels (Supplementary Fig. 5).
The fourth and fifth “medium” scenarios also project continued growth in freight digitalization and efficiency gains, but at
approximately half the rate of the optimistic scenarios, acknowledging real-world challenges to market uptake and the slow
operational efficiency improvements observed in developed digital freight markets2,9,19,21. Supplementary Fig. 7 illustrates the
projected future trends for key operational efficiency indicators for both MFT and HFT across the five scenarios, using the truck-
hailing sample’s values as target levels at market saturation.

Recognizing China’s commitment to decarbonize its road transport sector through low-carbon and fuel-efficient
technologies, we also developed two scenarios along the technological dimension. The current policy scenario (CPS) projects
the continuation of existing transport and energy policy initiatives. Conversely, the carbon neutrality scenario (CNS) envisions a
more aggressive decarbonization path, characterized by the accelerated deployment of ZEVs, including battery electric and
hydrogen fuel cell vehicles. Combining these logistical and technological changes, we established ten comprehensive cross-
cutting scenarios (Fig. 3). The current policy – business as usual scenario (CPS_BaU) serves as the baseline for comparison.
Detailed scenario parameters and methodologies are provided in the methods section.

Road freight transport decarbonization pathways along logistics and technological dimensions
Fig. 4a and 4b illustrate projected CO2 emission trajectories for China’s MHDT sector, comparing BaU scenarios with medium
and optimistic scenarios, respectively. Although road freight transport demand is expected to peak around 2040, CO2 emissions
are projected to peak much earlier, by 2031, due to increasing adoption of low-carbon and fuel-efficient technologies. Both
technological advancement and logistics improvements demonstrably contribute to this trend. Relying solely on accelerated
technology deployment, without further operational and logistics improvements, would advance the emission peak to 2028,
followed by a rapid decline (CNS_BaU). Integrating technology with logistics measures would further accelerate the peak, with
the timing dependent on truck-hailing service and operational efficiency growth. In medium scenarios (Fig. 4a), the MHDT
sector’s emissions peak by 2022 (CNS_S-shape_Med) and 2025 (CNS_PLinear_Med), while in optimistic scenarios (Fig. 4b), the
peak occurs by 2020 (CNS_S-shape_Opt) and 2024 (CNS_Linear_Opt).

Logistics improvement considerably contribute to emission reduction, particularly in the near term. Enhanced load
factor and vehicle empty running lead to lower travel demand and a much smaller vehicle stock (Supplementary Fig. 8). For
example, under the CPS_linear_Opt scenario, if China’s average vehicle empty running and annual VKT reach current U.S. levels
by 2030 (around 20% and 90,000km, respectively), the MHDT vehicle stock would decrease by over a third compared to 2020.
The near-term decarbonization impact of logistics improvements, relative to rapid low-carbon technology adoption, is
highlighted by the shaded areas in Fig. 4a and 4b. Compared to the CNS_BaU scenario, which assumes no logistics
improvements, rapid operational efficiency gains result in additional cumulative CO2 emission reductions of 2547 Mt (CPS_S-
shape_Med) and 476 Mt (CPS_Linear_Med) in the medium scenarios, and 3593 MT (CPS_S-shape_Opt) and 2398
(CPS_Linear_Opt) in the optimistic scenarios, before low-carbon technologies become the primary driver of emission reduction.

Synergies between logistics and technological improvements across varying timeframes
Fig. 5 analyzes the contributions of various factors to emission reductions in year-over-year comparison within each scenario
using the LMDI approach (methods), examining both the near-term (2020-2035) and long-term (2035-2060) changes. Eight
driving forces are considered: operation and logistics (empty running, load factor, payload capacity, VKT), technological factors
(fuel efficiency, low-carbon fuels), and other factors (demand, vehicle structure) (see methods). In the baseline scenario
(CPS_BaU), current policies, primarily focused on near-term targets, are insufficient for sustained decarbonization after 2035,
resulting in substantial residual emissions in the MHDT sector in 2060 (Fig. 5a). Aggressive deployment of low-carbon
technologies substantially reduces emissions, mainly in the long-term as technology adoptions accelerates (Fig. 5b). Conversely,
while technological improvement (fuel economy improvement and low-carbon fuel adoption) have limited near-term impact,
operational and logistics enhancements offer substantial near-term emissions reduction potential, driven by rapid growth of
truck hailing services, complementing technology’s role (Fig 5c, 5e, 5g and 5i).

With combined logistics and technology strategies, logistics yields the most immediate emission reductions, but
technology’s impact becomes dominant in the longer run (Fig. 5d, 5f, 5h and 5j). However, compared to the baseline scenario
(CPS_BaU), logistics plays an equally important role in long-term cumulative emission reductions (Fig. 6). This is because near-
term reductions in unnecessary travel, due to improved operational efficiencies (better load factor and lower empty running),
lessen the need for extensive ZEV deployment in the long run. The greater cumulative emission reduction achieved through
logistics improvements, compared to technological advancements across both timeframes, highlights another potential synergy
between the two decarbonization forces. Near-term operational and logistics efficiency improvements could reduce the long-
term mitigation costs associated with low-carbon technologies and infrastructure. This is reflected by the vehicle stock in the
CNS_Linear and CNS_S-shape scenarios being reduced to less than one third of the CNS_BaU scenario by 2060 in both medium
and optimistic projections (Supplementary Fig. 8).

The impact of operational performance limitations of ZEVs
Current medium- and heavy-duty ZEVs, such as battery electric trucks, often face limitations in range and payload capacity42.
This is supported by recent statistics from China, which indicates that electric and fuel cell trucks exhibit lower range and
payload performance compared to internal combustion engine (ICE) vehicles65-66. Consequently, our analysis incorporates the
assumption that ZEVs initially possess reduced range and capacity compared to ICE vehicles, with these performance gaps
gradually diminishing over the model period (methods). The impact of ZEVs’ operational limitations, coupled with the structural
shift towards electric and fuel cell powertrains, is not directly illustrated in Fig. 5, but is evident in Supplementary Fig. 9. Since
the average ZEV with performance limitations cannot achieve one-on-one replacement with their ICE counterparts43,
accelerated adoption of low-carbon technologies in the CNS scenarios results in lower average vehicle occupancy (tonnes per
vehicle) compared to all CPS scenarios. In the post-2035 period under optimistic projections, average vehicle occupancy also
declines because the decrease in average payload capacity outweighs the gains in load factor. As a result, additional vehicle
travel is required across all powertrains to meet the same aggregate demand in TKM, leading to increased use of diesel, hybrid
and NG vehicles (Supplementary Fig. 10) and higher tailpipe emissions.

Fig. 7 presents the emission results from counterfactual experiments, assuming no range and capacity limitations for
EVs and FCVs. These results suggest that ZEV performance penalties can lead to higher cumulative emissions in both near- and
long-term. Specifically, in the CNS scenarios with accelerated low-carbon technology deployment, cumulative carbon emissions
are approximately 3% higher from 2020-2035 and 10% higher from 2035-2060 compared to scenarios without range and
capacity penalties. The increase in emissions is primarily attributed to greater vehicle travel resulting from reduced average
loads due to decreased payload capacity. Conversely, the range limitation is offset by an increase in vehicle stock and does not
directly affect aggregate TVKT or total carbon emissions, once other operational efficiency parameters are considered.

Methods
Truck-hailing sample. Proprietary vehicle trip data were obtained from China’s leading truck-hailing service providers. The
dataset includes trip records from 2000 active vehicles operated by owner-operators who exclusively use digital freight platforms
to source freight. These data cover the period from October 1 to November 22, 2018, and contain a total of 51021 consecutive
road freight trips, reconstructed from millions of GPS location records. All digitally matched trips are single origin-destination (OD)
journeys, and do not include multi-stop journeys that involve multiple origins and destinations.

The dataset combines waybill, geospatial, and vehicle operation information. It contains key trip characteristics such as
OD locations, and distances of laden and unladen/empty journeys. It also covers vehicle and cargo attributes, including vehicle
type, payload capacity, and cargo weight. The trips span all regions of mainland China, encompassing 31 provinces and
municipalities (Supplementary Fig. 2), and cover all major public road types—expressways (tolled), national and provincial
highways, and other public roads. Supplementary Table 3 presents a breakdown of trips by duty cycle.

In this study, vehicle empty running is defined as the distance traveled between the destination of one trip and the
origin of the subsequent trip, as illustrated in Supplementary Fig. 3. The share of empty miles is calculated as the percentage of
this distance relative to the total travel distance.

Enhanced vehicle fleet model. Carbon emissions from road freight transport are estimated using the transport activity – modal
structure – energy intensity – fuel to carbon ratio approach37, as shown in Equation (1) below, where total emissions from the
medium- and heavy-duty sector are calculated as the sum of emissions across vehicle and powertrain types. This study considers
two vehicle types—medium freight truck (MFT) and heavy freight truck (HFT)—and five powertrain types: electric vehicle (EV),
hydrogen fuel cell electric vehicle (FCV), hybrid vehicle powered by diesel (HEV-diesel), diesel-fueled internal combustion engine
vehicle (ICEV-diesel) and natural gas vehicle (NGV). Other low-carbon fuel options, such as biofuels (e.g. bio-ethanol, bio-diesel,
and bio-dimethyl ether) and e-fuels produced from renewable energy, are excluded due to uncertainties regarding their future
availability and cost-competitiveness7,76.

C = \Sigma \Sigma vkt * S * FCR * F (1)

C denotes total CO2 emissions of the road freight sector; vkt is total vehicle-kilometers-travelled per vehicle; S represents the
vehicle stock; FCR is the average fuel consumption rate; and F refers to the fuel carbon intensity.

While previous models in integrated assessment modeling framework often overlook key operational efficiency metrics,
we enhance the existing modeling approach to explicitly incorporate critical operational factors—such as vehicle empty running,
load factor, VKT, and payload capacity. The vehicle stock for each type is determined not only by its role in the domestic road
freight system (i.e., vehicle structure), but also by utilization at the vehicle level, which is influenced by these operational factors
as shown in Equation (2). This approach aligns with findings from existing literature, which suggest that improvement in
operational efficiency can lead to reductions in fleet size23,30.

S = TKM / (vkt * (1-Emp) * LF * Cap) (2).

Here, TKM refers to the total tonne-kilometers traveled by a specific vehicle and powertrain type; LF denotes the average load
factor or payload capacity utilization; Cap represents the average payload capacity per vehicle. vkt is the total vehicle kilometers
traveled; Emp is the percentage of empty mileages; and vkt*(1-Emp) indicates laden, or revenue-generating, distance.

The model spans annually from 2020 to 2060. For base year (2020) calibration, total TKM and vehicle stock for the HDV
sector are sourced from National Bureau of Statistics71. Fuel consumption rates are from CATARC (2021)57. Payload capacity for
ICE-diesel, HEV-diesel and NGV vehicles are drawn from 2010 China Expressway Statistics63 and ICCT (2017)77. For EV and FCV,
payload capacity is from Feng (2022)66, which reports that ZEVs have approximately 15% and 10% lower payload capacity for HFT
and MFT, respectively, compared to diesel and NG vehicles.

Values for empty running ratio, load factor and VKT are derived on existing literature3,57,63 and authors’ calibration. VKT
data specific to EVs and FCVs are based on Wang and Liang (2023)65, which indicate ZEVs operate with roughly 25% lower VKT
than other powertrain types. Fuel carbon intensity data are obtained from IPCC’s Guidelines for National Greenhouse Gas
Inventories78.

The estimation of future TKM values follows the methodology outlined in Pan et al. (2018)79. Future energy
consumption rates are assumed to improve in line with projections from Lu et al. (2023)80 and SAE-China (2020)81. The future
stock of each powertrain type is calculated using total vehicle stock derived from Equation (2), vehicle survival curves, and
government targets for vehicle sales and phase-out by powertrain type. A uniform vehicle survival pattern is assumed across all
powertrain types, based on data from Lu et al. (2023)80 and Liu et al. (2020)82. Sales and retirement targets vary by powertrain
type depending on the technological scenario, and are described in detail in the scenario settings section.

Projections for vehicle empty running, load factor and VKT are determined by assumptions regarding the market
penetration of truck-hailing services and their effects on operational efficiency—these are also detailed in the scenario section.
The payload capacity of ICE-diesel, hybrid diesel and natural gas vehicles are assumed to stay constant for both MFTs and HFTs.
For zero-emission vehicles (ZEVs), including battery electric vehicles (EVs) and fuel cell vehicles (FCVs), performance gaps relative
to other powertrain types (ICE-diesel, HEV-diesel and NGV) are assumed to gradually narrow, with payload disadvantages halved
and VKT parity achieved by 2060 (Supplementary Fig. 4).

Decomposition of emission reduction by main contributing factors. Following Equation (1) and (2), the carbon emissions from
road freight transport for vehicle type i and powertrain type j can be attributed to several driving forces. These include operational
factors (empty running, load factor, payload capacity, VKT), technological factors (fuel efficiency and low-carbon technologies),
and other factors (freight transport demand, fuel carbon intensity, and overall vehicle structure):

C_ij = (C_ij / E_ij) * (E_ij / TVKT_ij) * TVKT_ij * (TVKT_ij / TVKT_Revenue_ij) * (TVKT_Revenue_ij / ATKM_ij) * (ATKM_ij / TKM_ij) * (TKM_ij / TKM_i) * (TKM_i / TKM) (3),

where TVKT refers to total vehicle-kilometers-traveled, calculated as the product of VKT and vehicle stock; TVKT_Revenue
represents the revenue-generating portion of TVKT; and ATKM denotes available capacity-kilometers54,83.

C/E captures the fuel carbon intensity, and E/TVKT reflects fuel consumption rate. The ratio TVKT/TVKT_Revenue is the
inverse of the revenue mileages share (i.e., one minus the share of empty mileage), and TVKT_Revenue/ATKM represents the
inverse of the average vehicle payload capacity. The ratio ATKM/TKM is the inverse of load factor. TKM /TKM and TKM/TKM
describe the distribution of freight activity across powertrain types (fuel structure) and vehicle types (vehicle structure),
respectively.

The contribution of each factor to changes in carbon emission over selected years is estimated using the LMDI
approach84-85:

\Delta C = \Sigma \Delta K_n (4),

\Delta K_n = (C_ij^t - C_ij^0) / (ln C_ij^t - ln C_ij^0) * ln (K_n^t / K_n^0) (5),

where K denotes the eight contributing factors in equation (3), and \Delta C and \Delta K represent the changes in carbon emissions and
n n
each contributing factor, respectively, between year 0 and year t.

Scenario settings. To evaluate the potential contributions of logistics and technology to decarbonizing China’s MHDT road freight
sector, we develop cross-cutting scenarios that integrate both pathways.

On the logistics side, we construct five scenarios representing possible future developments in truck-hailing services.
Each scenario is associated with corresponding changes in vehicle operational efficiency—specifically, improvements in empty
running, load factor, and VKT. The first scenario represents a business-as-usual (BaU) case, assuming that truck-hailing
penetration remains at its current level.

In the second and third scenarios, we adopt more “optimistic” assumptions about the future development of truck-
hailing in China, modeling its growth as linear and S-shaped trajectories, beginning in 2013. We take a market penetration rate
of 22% in 2020, based on the proportion of truck drivers who rely exclusively on digital freight platforms, as reported in a recent
truck driver employment survey51. We assume that truck-hailing services will eventually reach a saturation point at 80% market
penetration. In addition to digital freight matching, contract logistics has played an important—albeit more traditional—role in
China’s road freight sector, typically involving large commercial fleets and long-term contracts86-87. Due to a lack of
comprehensive statistics, we estimate the current market share of contract logistics at 11.4%, corresponding to the share of
drivers who report stable sources of freight according to the same survey51. We further assume that contract logistics began
developing in 2001, when China entered the World Trade Organization, and has since grown linearly toward a saturation level of
20%.

The fourth and fifth scenarios represent “medium” projections, assuming continued growth in freight digitalization—
following linear and S-shaped trajectories, respectively—but at roughly half the rate of the “optimistic” scenarios. Similarly, the
market adoption rate of contract logistics is also assumed to halve in these cases. Supplementary Fig. 5a presents the projected
market penetration rates of truck-hailing services and contract logistics across all five scenarios. In the optimistic projections,
truck-hailing services are expected to reach market saturation by 2039 under the linear growth model and by 2036 under the S-
shaped growth model, while contract logistics reaches saturation by 2038. In the medium projections, truck-hailing services reach
market saturation by 2057 and 2052 under the linear and S-shaped growth models, respectively, while contract logistics is
projected to plateau at 20% market penetration by 2056.

We assume that all vehicles with access to truck-hailing services in the future will achieve operational performance
levels—specifically in terms of empty running, load factor and VKT—comparable to those observed in the national truck-hailing
sample. Since our analysis indicates that owner-operators in the sample exhibit similar performance in empty mileages, VKT and
load factor to that of large commercial fleets or contract logistics, we make a further simplifying assumption: vehicles engaged in
contract logistics will perform similarly to truck-hailing users. As a result, average market operational efficiencies can be estimated
as the weighted averages of performance metrics from two groups—vehicles using truck-hailing services or contract logistics,
and the remaining owner-operators who rely on neither. In essence, average market operational efficiency is primarily driven by
the combined market shares of truck-hailing and contract logistics (Supplementary Fig. 5b).

We define two scenarios along the technological dimension. The current policy scenario (CPS) reflects a trajectory
shaped by existing and announced low-carbon technology focused policies for near-term targets. These include China’s National
Determined Contributions (NDCs), the New Energy Vehicle Industry Development Plan for 2021–203588, the Action Plan for Carbon
Dioxide Peaking Before 203089, and the Energy Conservation and New Energy Vehicle Technology Roadmap 2.081. While these
policies emphasize near-term progress through 2035, they fall short of supporting deep decarbonization beyond that timeframe.

In contrast, the carbon neutrality scenario (CNS) outlines a more ambitious pathway aligned with China’s long-term goal of
achieving carbon neutrality by 2060. This scenario assumes accelerated deployment of zero emission vehicles, including battery
electric and hydrogen fuel cell trucks, along with more aggressive sales targets for ZEVs and earlier market phase-out deadlines
for ICE and hybrid vehicles. These assumptions draw on projections from Feng (2022)66, iCET (2021)90, and SAE-China (2023)91. In
essence, the key difference between the CPS and CNS scenarios lies in the pace of low-carbon technology adoption, which in turn
drives divergence in vehicle powertrain composition.

Supplementary Fig. 6 presents the market share of all vehicle and powertrain types at five-year intervals under two
technological scenarios, while Supplementary Fig. 7 illustrates vehicle operational efficiency metrics across the combined logistics
and technological scenarios.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors estimate the effects of increased use of truck-hailing in China on freight decarbonization, finding that the combination of truck-hailing service expansion and low-emission vehicle adoption could, in optimistic cases, reduce China’s road freight emissions by about a third.\nThe central problem with this analysis is that it treats non-causal differences across data sets as though they are caused by truck-hailing. Specifically, observed differences in key performance factors (empty running, load factor and VKT) are compared across a proprietary truck hailing sample and alternative samples representing overall trucking (tolled expressway data, pilot projects, surveys). The truck-hailing sample consistently shows lower empty running time, higher load factors and higher VKT.\n“Vehicles utilizing truck-hailing services displayed 8-18 percentage points reduction in empty mileages, 1-14 percentage points increase in load factor, and 207-284 km rise in daily VKT levels (compared to data reported by MOT) compared to vehicles resorting to conventional freight matching practices.”\nThe analysis then assumes if truck-hailing were applied to trucks that are not currently using truck-hailing, their performance would improve to match those in the truck-hailing sample:\n“We also assume that in these two scenarios, the continued market uptake of the digital logistics service may lead to systemic operational and logistics improvements, which are reflected by a reduced share of empty mileages, a boosted load factor, and enhanced VKT levels.”\nThis is a problem because the authors cannot differentiate from the data they have the effect of each of the following on the differences observed:\n• Causality: Truck-hailing causes improvements in empty running, load factor and VKT\n• Reverse Causality: Users that already have better than average empty running, load factor and VKT are systematically more likely to adopt truck-hailing because those are the ones that truck-hailing can benefit most\n• Confounding Variables: A third omitted variable (e.g.: freight type, client type, transportation zone, etc.) might influence both performance (empty running, load factor and VKT) and propensity to use truck-hailing services\nFurthermore, the authors are effectively assuming homogeneous treatment effects, whereas what often happens in practice when a new technology is introduced is that the applications that are well-suited to that technology (truck-hailing can improve performance) switch and the applications that are not well-suited to the technology (truck-hailing would not improve performance) do not switch. So, there is an additional problem with the approach:\n• Heterogeneous Treatment Effects: The users that choose to use truck-hailing are precisely the ones who benefit from truck-hailing; if other users were to adopt truck-hailing, they would not benefit (as much or at all).\nThe authors state in the discussion that “our estimates based on the sample by no means reflect the direct causal effects of truck-hailing on the operational performances of its users.” But if these differences are not causal, then the authors have no basis for using them to project operational or emissions implications of scenarios that apply truck hailing to more trucks.\nThe bottom line is that the authors’ data in Table 1 is descriptive, not causal. The truck-hailing sample has better stats than the other samples, but we don’t know why, so there is no basis for assuming that if truck-hailing were used with trucks that don’t currently use truck-hailing that their stats would improve.\nI can imagine the authors posing this as a “what if” scenario (e.g.: “what if the differences we observe were caused by truck-hailing and the same improvements would occur if truck-hailing were applied to trucks not currently using truck-hailing”), but such a “what if” analysis is too speculative and inconclusive for a journal like Nature Communications.\nI recommend the authors either (1) seek additional data, experiments, causal inference, theory/models, or simulations to persuasively establish the degree to which truck-hailing actually causes the differences observed or (2) frame the study as a speculative what-if analysis and submit to a journal that would be a fit for such an analysis.\nAdditional items\n- Fig 2: Explain why such a large portion of the data are over capacity.\n- Fig 2: Explain how the y-axis values are calculated. Are they distance-weighted? I assume they are only measuring laden trips and ignoring deadheading, but I did not see this stated.\n- Fig 2: What do these same stats look like for contract trucks that don’t use truck-hailing?\n- Table 1: In the data you are using, are you sure that all empty running is recorded? For example, the truck-hailing apps would only have information about the truck when they are using the app, so if they do not always use the app when traveling empty, the reported numbers could be dramatically inflated.\n- When the authors use language like “resorting to conventional freight matching practices,” they reveal that they have already decided that truck-hailing is superior to conventional and that truckers only use conventional if they are “resorting” to it. This seems to be a belief that is not justified by the analysis.\n- “This may result in higher aggregate TVKT (total vehicle-kilometers-traveled, which equals VKT multiplied by vehicle stock) by diesel, hybrid and NG vehicles (Fig. S10) and eventually greater tailpipe emissions.” It is clear that limited range, limited payload electric trucks may require more VKT to move the same amount of goods than diesel trucks, but it is not clear why adoption of limited-range electric trucks, which displace at least some diesel trips, would lead to increased diesel vehicle travel.\n- “our findings on the decarbonization benefits may be considered as a possible upper limit for emission reduction effects associated with the new digital freight services.” It’s fine to compute bounds, but the whole framing of this paper is that truck-hailing could have huge effects, larger than fuel switching. So to then say “the reality is likely smaller than the numbers we calculate,” undercuts the key message. A lower bound on the change would be more valuable.\n- “Hence, in the event of trucking efficiency improvement, there could be strong rebound effects and additional induced demand for road freight, and possible further substitution of less carbon-intensive modes including rail and shipping. These undesirable outcomes may reduce the climate benefits of efficiency gains in trucking. Under such circumstances, policymakers may need to employ price instruments (e.g. fuel tariffs and carbon taxes) and non-price regulations to tackle the unintended behavioral consequences.” These effects may be “undesirable” from the perspective of climate goals, but there are also benefits here (e.g.: induced demand means more people gain the benefits of the goods they consume). So, any claim about what is good or bad needs to be caveated with respect to a particular policy goal or target.", "reviewer_2": "This paper examines an emerging business model, referred to as truck-hailing, which connects road freight carriers with shippers through mobile applications, similar to ride-hailing platforms. Since its inception around a decade ago, these business models have gained considerable traction in China, reaching notable market penetration and supporting millions of journeys annually. Using a proprietary national truck-hailing dataset and a modified vehicle fleet model that incorporates operational factors, they explore the potential decarbonization impacts of truck-hailing and enhanced operational efficiency across various scenarios in China. In an optimistic scenario that combines the growth of truck-hailing services with low-carbon technology adoption, the authors suggest that improvements in load utilization and reductions in empty vehicle runs could significantly decrease China’s road freight emissions over both near- and long-term periods, compared to a business-as-usual scenario. However, they also indicate that limitations in zero-emission vehicle capacity may offset some of these gains, leading to modest increases in emissions in both the short and long term.\nThis paper has a very important ambition and builds on significant empirical data. On the other hand, there are several concerns about research design that need to be carefully addressed to improve the validity of its conclusions.\n1. Positioning in literature should be improved: While I like the term truck-hailing, it creates the impression as if this was a hardly researched phenomenon – which is not the case. The term should be connected to very related research on freight (matching) platforms and the Physical Internet. Heinbach et al. (2022) even provide a review of these approaches and Zhou and Wan (2022) explore their impact on the sector.\n2. The current methodology should be extended to consider crucial operational constraints: Since the early days of horizontal collaboration research a few decades ago, people have dreamed of filling all the empty spaces on trucks and avoiding idle trips with different forms of collaboration. However, the real world shows that there are numerous operational constraints (e.g., time windows, driving hours, technical compatibilities, and many more; for a comprehensive overview of these limitations see Basso et al. (2018)) that need to be satisfied to realize this savings potential. In the submitted work, the savings potential is estimated in Equation 3. However, related literature shows that the operational constraints determine the savings potential in each case study and easily lead to deviations of around 50%. Therefore, it is essential to consider these constraints in order to derive reliable conclusions for the decarbonization pathways later on. See, for instance, Los et al. (2022) as an example study that investigates the savings potential under consideration of crucial operational constraints based on large-scale collaboration and empirical data from a freight matching platform.\n3. The climate/decarbonization analysis should be refined: Firstly, I think it is better not to try to discuss climate implications, but decarbonization targets. Otherwise, you would need to incorporate more background information on the underlying climate models. Secondly, I think the different drive systems and process design should be considered from a life-cycle perspective. Horvath et al. (2006) might serve as a good reference in that context.\nReferences\nBasso, F., D'Amours, S., Rönnqvist, M., & Weintraub, A. (2019). A survey on obstacles and difficulties of practical implementation of horizontal collaboration in logistics. International Transactions in Operational Research, 26(3), 775-793.\nHeinbach, C., Beinke, J., Kammler, F., & Thomas, O. (2022). Data-driven forwarding: a typology of digital platforms for road freight transport management. Electronic Markets, 32(2), 807-828.\nHorvath, A. (2006). Environmental Assessment of Freight Transportation in the US (11 pp). The International Journal of Life Cycle Assessment, 11, 229-239\nLos, J., Schulte, F., Gansterer, M., Hartl, R. F., Spaan, M. T., & Negenborn, R. R. (2022). Large-scale collaborative vehicle routing. Annals of Operations Research, 1-33.\nZhou, Z., & Wan, X. (2022). Does the sharing economy technology disrupt incumbents? Exploring the influences of mobile digital freight matching platforms on road freight logistics firms. Production and Operations Management, 31(1), 117-137."}, "author_response": "Response to Reviewers’ Comments for Manuscript NCOMMS-24-58987-T\nentitled “The potential role of truck-hailing and operational efficiency\nimprovement in decarbonizing China’s medium- and heavy-duty road freight\ntransport”\nWe sincerely appreciate the reviewers’ interest and support in our research, and\nare very grateful for the constructive comments and suggestions. We have\nrevised the manuscript accordingly. Please find below our point-by-point\nresponse to each comment.\nReviewer #1 (Remarks to the Author):\nComment:\nThe authors estimate the effects of increased use of truck-hailing in China on\nfreight decarbonization, finding that the combination of truck-hailing service\nexpansion and low-emission vehicle adoption could, in optimistic cases, reduce\nChina’s road freight emissions by about a third.\nThe central problem with this analysis is that it treats non-causal differences\nacross data sets as though they are caused by truck-hailing. Specifically,\nobserved differences in key performance factors (empty running, load factor and\nVKT) are compared across a proprietary truck hailing sample and alternative\nsamples representing overall trucking (tolled expressway data, pilot projects,\nsurveys). The truck-hailing sample consistently shows lower empty running time,\nhigher load factors and higher VKT.\n“Vehicles utilizing truck-hailing services displayed 8-18 percentage points\nreduction in empty mileages, 1-14 percentage points increase in load factor, and\n207-284 km rise in daily VKT levels (compared to data reported by MOT)\ncompared to vehicles resorting to conventional freight matching practices.”\nThe analysis then assumes if truck-hailing were applied to trucks that are not\ncurrently using truck-hailing, their performance would improve to match those in\nthe truck-hailing sample:\n“We also assume that in these two scenarios, the continued market uptake of\nthe digital logistics service may lead to systemic operational and logistics\nimprovements, which are reflected by a reduced share of empty mileages, a\nboosted load factor, and enhanced VKT levels.”\nThis is a problem because the authors cannot differentiate from the data they\nhave the effect of each of the following on the differences observed:\nInternal\n• Causality: Truck-hailing causes improvements in empty running, load factor\nand VKT\n• Reverse Causality: Users that already have better than average empty running,\nload factor and VKT are systematically more likely to adopt truck-hailing because\nthose are the ones that truck-hailing can benefit most\n• Confounding Variables: A third omitted variable (e.g.: freight type, client type,\ntransportation zone, etc.) might influence both performance (empty running,\nload factor and VKT) and propensity to use truck-hailing services\nResponse:\n Added a brief literature review on the causal link between truck-hailing\nand operational efficiency.\n Removed any statements implying causality from sample-reference\ncomparisons.\n Introduced additional scenarios to address uncertainties in future\ndevelopments and impacts of truck-hailing.\nWe sincerely appreciate this insightful comment. While we included relevant\nstudies on the causality between truck-hailing and operational efficiency\nimprovements in the supplementary material, we recognize that the absence of\nsuch discussion in the main text may have led to confusion.\nThe causality relationship between truck-hailing—a form of horizontal\ncollaborative transport solutions – and road freight operational efficiency gains,\nhas been well documented in operational research and logistics literature (ref.\n68-70, 78-79, 94). Existing research shows that horizontal collaboration is one of\nthe most effective methods for enhancing operational efficiency and\nsustainability of freight transport, primarily due to asset sharing and route\noptimization. Building upon this foundation, the primary objective and\ninnovation of our study, therefore, is to assess how such efficiency\nimprovements could impact sectoral decarbonization pathways—an important\naspect that remains underexplored in current integrated assessment modeling\n(IAM) frameworks and the transport decarbonization literature—utilizing a large-\nscale real-world data and an enhanced vehicle fleet model that incorporates key\noperational factors.\nIn the revised manuscript, we have addressed the previously missing literature\nby including a brief review in the 3rd paragraph of the introduction section. This\naddition helps better connect our research to the broader body of work on\nhorizontal collaborative transport and truck-hailing, and provides a clearer\ncontext for our study’s motivation.\nAs such, the omission of relevant literature in the introduction may have\ncontributed to a lack of clarity in how we framed the comparison between\nsample results and reference statistics in the first part of the results section.\nInternal\nSuch descriptive comparison was not intended to infer causality, as similar\nresearch exists and is beyond the scope of our study. Rather, it highlights that\ntruck-hailing users exhibit better operational performance than non-users, which\nis consistent with findings from existing literature (supplementary ref. 15-16, 19-\n22).\nTo avoid confusion, we removed the statement regarding the percentage point\ncomparison between our sample and reference statistics in the revised\nmanuscript, as it was neither intended to imply causal effects or truck-hailing\nservices nor used in our macro-level emission estimates. Instead, baseline\nvalues for the key operational indicators in our enhanced vehicle fleet model are\ndrawn from existing literature and model calibration. Only the operational\nperformance values of truck-hailing users in our model are assumed to match\nthose observed in the sample. Emission impacts of the operational efficiency\nimprovements are then evaluated through scenario analysis.\nIn response to your comment, and recognizing the uncertainty around truck-\nhailing’s effects—given the wide range of estimated benefits in literature and\nreal-world implementation challenges—we have expanded our scenario set. In\naddition to the previously emphasized optimistic scenarios, the revised\nmanuscript now includes several “median” scenarios that reflect slower\ndigitalization and more modest efficiency gains. These additions aim to capture\na wider range of plausible outcomes and better represent the uncertainty\ninherent in projecting the future development of truck-hailing.\nWe hope these revisions clarify our approach and address your concerns\nregarding the assumptions and robustness of our analysis.\nComment:\nFurthermore, the authors are effectively assuming homogeneous treatment\neffects, whereas what often happens in practice when a new technology is\nintroduced is that the applications that are well-suited to that technology (truck-\nhailing can improve performance) switch and the applications that are not well-\nsuited to the technology (truck-hailing would not improve performance) do not\nswitch. So, there is an additional problem with the approach:\n• Heterogeneous Treatment Effects: The users that choose to use truck-hailing\nare precisely the ones who benefit from truck-hailing; if other users were to adopt\ntruck-hailing, they would not benefit (as much or at all).\nResponse:\nThank you for highlighting the importance of heterogeneous treatment effects.\nWe fully acknowledge that in practice, the adoption of new technologies like\ntruck-hailing is often driven by self-selection—users who are better positioned to\nInternal\nbenefit are more likely to adopt, while those who are less suited may not.\nAs mentioned in our earlier response, the operational research and logistics\nliterature provides strong evidence that truck-hailing and similar forms of\nhorizontal collaboration can significantly enhance operational efficiency.\nHowever, we agree that these benefits are not uniformly distributed. The\npresence of heterogeneous treatment effects suggests that average\nimprovements across the broader population of users may be smaller than those\nobserved in early adopters. This observation is consistent with empirical findings\nfrom mature digital freight markets such as Europe, where rapid platform\nadoption has not always translated into proportional efficiency gains (see ref.\n68).\nTo account for this, the revised manuscript includes four additional “median”\nscenarios that assume slower growth in both freight digitalization and\noperational efficiency. These scenarios help reflect the potential variability in\ntreatment effects across different user groups. Details of these scenarios are\nprovided in the Methods section, with key results presented in the Results\nsection.\nComment:\nThe authors state in the discussion that “our estimates based on the sample by\nno means reflect the direct causal effects of truck-hailing on the operational\nperformances of its users.” But if these differences are not causal, then the\nauthors have no basis for using them to project operational or emissions\nimplications of scenarios that apply truck hailing to more trucks.\nResponse:\nThank you for highlighting this important point. We agree that the original\nstatement created a logical inconsistency and have removed it from the revised\nmanuscript. As discussed in our earlier responses, the causal relationship\nbetween truck-hailing and improvements in operational efficiency is well-\nsupported in the operational research and logistics literature. Our study builds\non this established foundation to explore the potential implications of such\nefficiency gains for China's road freight decarbonization pathways, using large-\nscale, real-world empirical data. Please refer to our previous responses for a\ndetailed discussion and supporting references.\nComment:\nThe bottom line is that the authors’ data in Table 1 is descriptive, not causal. The\ntruck-hailing sample has better stats than the other samples, but we don’t know\nwhy, so there is no basis for assuming that if truck-hailing were used with trucks\nInternal\nthat don’t currently use truck-hailing that their stats would improve.\nResponse:\nThank you for raising this important point. As discussed in our first response, our\nanalysis is informed by existing literature that supports a causal link between\ndigital freight services and operational efficiency improvements. Therefore, the\ndescriptive comparison in Table 1 is not intended to infer causality. Instead, the\nsample results were compared with the best available reference data to\ndemonstrate that truck-hailing users exhibit better operational performance,\nwhich is consistent with findings from existing literature. Please see our first\nresponse for a more detailed discussion.\nComment:\nI can imagine the authors posing this as a “what if” scenario (e.g.: “what if the\ndifferences we observe were caused by truck-hailing and the same\nimprovements would occur if truck-hailing were applied to trucks not currently\nusing truck-hailing”), but such a “what if” analysis is too speculative and\ninconclusive for a journal like Nature Communications.\nResponse:\nThank you for this thoughtful comment. We appreciate the concern regarding the\nspeculative nature of “what-if” analyses and fully acknowledge the importance\nof clearly distinguishing between correlation and causation. As noted in our\nearlier response, we have addressed the causality question in greater detail\nthere.\nThe primary objective of our study is to explore how improvements in operational\nefficiency could influence carbon emissions and sectoral decarbonization\npathways in China’s medium- and heavy-duty truck (MHDT) sector—a topic that\nremains underexamined in the transport decarbonization literature. In this\ncontext, we believe that scenario analysis—essentially a “what-if” approach—is\na well-established and valuable tool for navigating future uncertainties and\ninforming policy and investment decisions. Many influential climate and\nenvironmental science studies have used scenario-based methodologies to\nexplore plausible futures (e.g., Sala et al., 2000; Rockström et al., 2009; O’Neill\net al., 2017).\nGiven the rapid expansion of truck-hailing services in China, our analysis\nincludes three core scenarios—business-as-usual, median, and optimistic—\nrepresenting different trajectories of logistics digitalization and operational\nefficiency improvements. Notably, our assumptions around the future uptake of\nzero-emission vehicle technologies (e.g., battery electric and hydrogen fuel cell\ntrucks) are also “what-if” in nature, as these technologies are still in early stages\nInternal\nof development and deployment.\nIn light of the uncertainties surrounding both operational efficiency\nimprovements and the adoption of low-carbon technologies, we believe an\nintegrated scenario analysis provides valuable insights for understanding\npotential sectoral decarbonization outcomes. This approach allows us to assess\na range of plausible futures and better inform stakeholders navigating this\nevolving landscape.\nComment:\nI recommend the authors either (1) seek additional data, experiments, causal\ninference, theory/models, or simulations to persuasively establish the degree to\nwhich truck-hailing actually causes the differences observed or (2) frame the\nstudy as a speculative what-if analysis and submit to a journal that would be a fit\nfor such an analysis.\nResponse:\nThank you for this thoughtful suggestion. In line with your recommendation (1),\nwe have reviewed additional literature that examines the causal relationship\nbetween truck-hailing and improvements in operational efficiency. To strengthen\nthe theoretical foundation of our study, we have incorporated a brief summary of\nthis literature into the introduction (third paragraph) of the revised manuscript.\nThis addition helps contextualize our assumptions and better support the logic\nof our scenario-based approach. Please refer to our earlier response for further\ndetails.\nAdditional items\nComment:\n- Fig 2: Explain why such a large portion of the data are over capacity.\nResponse:\nThank you for this observation. Overloading is a common practice in China, as\nwell as in many other developing countries, largely due to the low barriers to\nmarket entry and intense competition among the large number of owner-\noperators who make up the majority of fleet carriers. This competitive\nenvironment often leads to low profit margins, prompting operators to overload\ntheir vehicles in order to maintain sufficient income (see refs. 3, 5, 7, and\nsupplementary ref. 11). We appreciate you highlighting this point, and we have\nadded an explanation in the text below Fig. 2 to clarify this issue.\nComment:\nInternal\n- Fig 2: Explain how the y-axis values are calculated. Are they distance-weighted?\nI assume they are only measuring laden trips and ignoring deadheading, but I did\nnot see this stated.\nResponse:\nThank you for this helpful comment. The y-axis values in Fig. 2 are calculated as\nfollows: first, all observations are grouped into bins based on the x-axis variable\n(i.e., vehicle capacity). For example, observations within the range of 1.01 to 2\ntons are grouped into the “2t” bin, those from 2.01 to 3 tons into the “3t” bin, and\nso forth. Box plots are then generated for each vehicle capacity bin.\nAccordingly, Figs. 2a and 2b illustrate the distribution of cargo weight and laden\ntrip distance, respectively, for each vehicle capacity category. These figures are\nnot distance-weighted. Fig. 2b presents only the distribution of laden distances\n(i.e., trips with cargo) and does not include information on empty or deadheading\ntrips. We have clarified this point in the figure caption in the revised manuscript.\nComment:\n- Fig 2: What do these same stats look like for contract trucks that don’t use\ntruck-hailing?\nResponse:\nThank you for this important question. Figure 2 presents the distributions of\ncargo weight and trip distance (i.e., laden distance between origin and\ndestination) across vehicle capacity categories based on our truck-hailing micro\ndataset. To the best of our knowledge, comparable micro-level distribution\nstatistics for fleets engaged in contract logistics in China are not publicly\navailable. This is likely due to the proprietary nature of such data and the\nrelatively limited research focus on this market segment.\nFor Figure 2a, however, the toll expressway data used in Table 1b may serve as a\nuseful proxy for contract logistics vehicles. In China, high toll fees tend to deter\nowner-operators and non-contract carriers from using expressway. In contrast,\ncontract trucks—often operated by large commercial fleets—are more likely to\nuse them due to their emphasis on timeliness and stronger financial margins\n(ref. 5). According to this dataset, the average load factor is 73% for medium\nfreight trucks (MFTs) and 87% for heavy freight trucks (HFTs). These values\nsuggest performance levels comparable to those of truck-hailing vehicles,\nparticularly in the case of HFTs.\nFor Figure 2b, origin-destination (OD) trip distance data for contract logistics is\nnot publicly available, as such information is highly dependent on specific\ndelivery tasks and is typically considered commercially sensitive. Nevertheless,\nseveral studies do highlight differences in fleet characteristics between large\nInternal\ncommercial carriers (more likely to engage in contract logistics) and owner-\noperators. For instance, a survey by the China Federation of Logistics &\nPurchasing (CFLP, 2020) indicates that large commercial fleets tend to have\nhigher average carrying capacities and a greater share of tractor-trailers.\nSimilarly, a study by G7—a leading digital logistics service provider—found that\nlarge fleets are more likely to use high-quality imported vehicles and have longer\ndaily operational hours compared to small carriers (ref. 36). Together, these\nfindings suggest that trucks used in contract logistics tend to have larger\ncapacity vehicles. However, their actual laden distances remain task-specific.\nWe hope this response provides useful context and helps clarify the data\navailability and comparability challenges.\nComment:\n- Table 1: In the data you are using, are you sure that all empty running is\nrecorded? For example, the truck-hailing apps would only have information\nabout the truck when they are using the app, so if they do not always use the app\nwhen traveling empty, the reported numbers could be dramatically inflated.\nResponse:\nThank you for this important and insightful question. We shared the same\nconcern during the data acquisition process and took specific measures to\nensure data reliability.\nDuring data collection, we verified that all participating truck drivers exclusively\nused the truck-hailing platform for their delivery operations, eliminating the\npossibility of untracked, off-platform journeys. Additionally, the dataset includes\ncomplete GPS tracking, allowing us to accurately differentiate between laden\nand empty segments of each trip. We used a combination of waybill data (which\nincludes origin and destination information), GPS coordinates, and vehicle\nspeed profiles to identify and verify all relevant travel activity—both laden and\nempty—while filtering out any unrelated movements.\nTo further validate our estimates of empty running, we cross-checked the results\nwith other data sources. Our findings are consistent with performance figures\nreported by large commercial fleets in China, which also show an average empty\nrunning ratio of approximately 10% (ref. 32). Moreover, our estimates align\nclosely with those reported by several EU countries—such as Portugal, Finland,\nSpain, Hungary, Greece, Denmark, and Belgium—where empty running ratios for\ncomparable international journeys range from 6.5% to 10.8%, and with the EU-\n27 average of 12.2% in 2018 (Eurostat 2022).\nGiven this alignment with both domestic and international benchmarks, we\nbelieve our estimates of empty running fall within a reasonable and credible\nInternal\nrange.\nComment:\n- When the authors use language like “resorting to conventional freight matching\npractices,” they reveal that they have already decided that truck-hailing is\nsuperior to conventional and that truckers only use conventional if they are\n“resorting” to it. This seems to be a belief that is not justified by the analysis.\nResponse:\nThank you for this helpful comment. As noted in earlier responses, the omission\nof relevant literature in the introduction may have contributed to a lack of clarity\nin how we framed the comparison between conventional freight matching and\ntruck-hailing.\nOperational research and logistics studies from both European and Chinese\ncontexts have consistently characterized conventional freight matching\npractices as analog, fragmented, and relatively inefficient—often involving\nopaque pricing mechanisms and limited integration of modern technologies (see\nrefs. 69, 2, and Supplementary Note 1). In contrast, truck-hailing platforms\nleverage mobile internet technologies to facilitate horizontal collaboration\namong carriers, and have been widely recognized as effective tools for improving\noperational efficiency and sustainability in freight transport.\nTo address this concern, we have added a concise literature review to the third\nparagraph of the introduction, clarifying the basis for this comparison. In\naddition, we have revised the manuscript to replace the phrase “resorting to”\nwith the more neutral “relying on” throughout the text, to avoid any unintended\nimplication of bias.\nComment:\n- “This may result in higher aggregate TVKT (total vehicle-kilometers-traveled,\nwhich equals VKT multiplied by vehicle stock) by diesel, hybrid and NG vehicles\n(Fig. S10) and eventually greater tailpipe emissions.” It is clear that limited range,\nlimited payload electric trucks may require more VKT to move the same amount\nof goods than diesel trucks, but it is not clear why adoption of limited-range\nelectric trucks, which displace at least some diesel trips, would lead to\nincreased diesel vehicle travel.\nResponse:\nThank you for this insightful question. The increase in total vehicle-kilometers-\ntraveled (TVKT) by diesel, hybrid, and natural gas (NG) vehicles is primarily due to\nthe payload limitations of current zero-emission vehicles (ZEVs). Due to these\nInternal\npayload penalties, ZEVs are unable to fully replace internal combustion engine\n(ICE) vehicles on a one-on-one basis (ref. 66). As a result, the overall average\nvehicle occupancy declines (Fig. S9), necessitating additional vehicle travel\nacross all powertrains—both conventional and zero-emission—to meet the\nsame aggregate demand in TKM. This dynamic explains the observed increase in\naggregate TVKT by non-ZEVs, as illustrated in Fig. S10. We have revised the\ncorresponding section of the main text to clarify this mechanism.\nComment:\n- “our findings on the decarbonization benefits may be considered as a possible\nupper limit for emission reduction effects associated with the new digital freight\nservices.” It’s fine to compute bounds, but the whole framing of this paper is that\ntruck-hailing could have huge effects, larger than fuel switching. So to then say\n“the reality is likely smaller than the numbers we calculate,” undercuts the key\nmessage. A lower bound on the change would be more valuable.\nResponse:\nThank you for this valuable observation. We agree that the original wording may\nhave inadvertently weakened the central message of the paper. We have\nremoved the statement from the revised manuscript to avoid misinterpretation.\nWe believe this revision better aligns the narrative with our core findings and\nensures a more consistent presentation of the study’s implications.\nComment:\n- “Hence, in the event of trucking efficiency improvement, there could be strong\nrebound effects and additional induced demand for road freight, and possible\nfurther substitution of less carbon-intensive modes including rail and shipping.\nThese undesirable outcomes may reduce the climate benefits of efficiency gains\nin trucking. Under such circumstances, policymakers may need to employ price\ninstruments (e.g. fuel tariffs and carbon taxes) and non-price regulations to\ntackle the unintended behavioral consequences.” These effects may be\n“undesirable” from the perspective of climate goals, but there are also benefits\nhere (e.g.: induced demand means more people gain the benefits of the goods\nthey consume). So, any claim about what is good or bad needs to be caveated\nwith respect to a particular policy goal or target.\nResponse:\nThank you for this thoughtful comment. We have revised the wording in the\nmanuscript by replacing “undesirable” with “unintended” to avoid normative\njudgment and better reflect the context of policy analysis. Additionally, we have\nclarified that the discussion pertains specifically to decarbonization policy goals,\nInternal\nrather than making general statements about the broader social or economic\nimpacts of induced demand. We hope this revision appropriately addresses your\nconcern and improves the precision of our argument.\nReferences\n1. China Federation of Logistics & Purchasing (CFLP). 2020. 2020 Report on\nStar-Rated Road Freight Fleets in China.\nhttp://www.cn156.com/cms/yanjiu/101761.html\n2. EUROSTAT. 2022. Road freight transport vehicle movements by loading\nstatus, type of transport and territorial coverage (vehicle-km, journeys) -\nannual data.\nhttps://ec.europa.eu/eurostat/databrowser/explore/all/transp?lang=en&s\nubtheme=road.road_go&display=list&sort=category&extractionId=ROAD\n_GO_TA_VM\n3. O’Neil, B.C., et al., 2017. The roads ahead: Narratives for shared\nsocioeconomic pathways describing world futures in the 21st century.\nGlobal Environmental Change. 42, 169-180\n4. Rockström, J., Steffen, W., Noone, K.et al. 2009.A safe operating space\nfor humanity.Nature461, 472–475\n5. Sala, O.E.et al., 2000.Global Biodiversity Scenarios for the Year 2100.\nScience 287,1770-1774\nInternal\nReviewer #2 (Remarks to the Author):\nComment:\nThis paper examines an emerging business model, referred to as truck-hailing,\nwhich connects road freight carriers with shippers through mobile applications,\nsimilar to ride-hailing platforms. Since its inception around a decade ago, these\nbusiness models have gained considerable traction in China, reaching notable\nmarket penetration and supporting millions of journeys annually. Using a\nproprietary national truck-hailing dataset and a modified vehicle fleet model that\nincorporates operational factors, they explore the potential decarbonization\nimpacts of truck-hailing and enhanced operational efficiency across various\nscenarios in China. In an optimistic scenario that combines the growth of truck-\nhailing services with low-carbon technology adoption, the authors suggest that\nimprovements in load utilization and reductions in empty vehicle runs could\nsignificantly decrease China’s road freight emissions over both near- and long-\nterm periods, compared to a business-as-usual scenario. However, they also\nindicate that limitations in zero-emission vehicle capacity may offset some of\nthese gains, leading to modest increases in emissions in both the short and long\nterm.\nThis paper has a very important ambition and builds on significant empirical data.\nOn the other hand, there are several concerns about research design that need to\nbe carefully addressed to improve the validity of its conclusions.\nResponse:\nThank you very much for your very positive feedback on our research objectives\nand empirical data. We are pleased that you share our view on the significant\nliterature gap regarding the role of logistics and operational efficiency\nimprovements in mitigating emissions from the road freight sector, particularly\nwithin the broader context of transport decarbonization research and integrated\nassessment modeling (IAM). While such demand-side strategies are being\nincreasingly highlighted—most notably in Chapter 5 of the Sixth IPCC\nAssessment Report—they are yet to be fully incorporated into IAM frameworks,\nlargely due to model limitations and data gaps. By leveraging large-scale\noperational data and developing an enhanced vehicle fleet model that explicitly\nrepresents key operational efficiency factors, our work aims to help bridge this\nimportant research gap.\nComment:\n1. Positioning in literature should be improved: While I like the term truck-hailing,\nit creates the impression as if this was a hardly researched phenomenon – which\nInternal\nis not the case. The term should be connected to very related research on freight\n(matching) platforms and the Physical Internet. Heinbach et al. (2022) even\nprovide a review of these approaches and Zhou and Wan (2022) explore their\nimpact on the sector.\nResponse:\nThank you for this thoughtful and constructive comment. We have carefully\nrevisited the relevant literature, including the studies you highlighted. Indeed, the\nliterature has well documented digital freight matching platforms—often referred\nto as truck-hailing in our manuscript—and their role as horizontal collaborative\nsolutions in freight transport. These platforms have been shown to improve\noperational efficiency through better freight matching, load consolidation, and\nasset utilization.\nTo better align our terminology and positioning with this literature, we have added\na brief review in the third paragraph of the introduction. This revision connects our\nstudy more clearly to existing work, while highlighting our novel contribution in\nanalyzing the macro-level emission implications of such platforms in China.\nComment:\n2. The current methodology should be extended to consider crucial operational\nconstraints: Since the early days of horizontal collaboration research a few\ndecades ago, people have dreamed of filling all the empty spaces on trucks and\navoiding idle trips with different forms of collaboration. However, the real world\nshows that there are numerous operational constraints (e.g., time windows,\ndriving hours, technical compatibilities, and many more; for a comprehensive\noverview of these limitations see Basso et al. (2018)) that need to be satisfied to\nrealize this savings potential. In the submitted work, the savings potential is\nestimated in Equation 3. However, related literature shows that the operational\nconstraints determine the savings potential in each case study and easily lead to\ndeviations of around 50%. Therefore, it is essential to consider these constraints\nin order to derive reliable conclusions for the decarbonization pathways later on.\nSee, for instance, Los et al. (2022) as an example study that investigates the\nsavings potential under consideration of crucial operational constraints based\non large-scale collaboration and empirical data from a freight matching platform.\nResponse:\nThank you for this insightful comment. As we reviewed additional literature, we\nfound evidence that in some digital freight markets—such as those in Europe—\noperational efficiency improvements have progressed more slowly than initially\nanticipated, despite rapid growth in online freight platforms (ref. 68). This\nobservation underscores the importance of accounting for real-world\noperational constraints,\nInternal\nIn line with your suggestion, we have expanded our scenario analysis to better\nreflect these practical challenges. Specifically, we introduced four additional\n“median” scenarios that assume freight digitalization and operational efficiency\nimprovements occur at roughly half the rate of those in the optimistic scenarios.\nThese scenarios are designed to capture a more realistic range of potential\noutcomes, recognizing that full efficiency potential may not be realized due to\nvarious logistical and regulatory constraints. Details of the revised scenarios are\nprovided in the Methods section, with corresponding results presented in the\nResults section.\nComment:\n3. The climate/decarbonization analysis should be refined: Firstly, I think it is\nbetter not to try to discuss climate implications, but decarbonization targets.\nOtherwise, you would need to incorporate more background information on the\nunderlying climate models. Secondly, I think the different drive systems and\nprocess design should be considered from a life-cycle perspective. Horvath et al.\n(2006) might serve as a good reference in that context.\nResponse:\nThank you for these valuable suggestions.\nIn response to your first point, we have revised the manuscript to focus on\nemission implications and decarbonization targets, rather than broader climate\nimplications. This ensures that the discussion remains grounded in the scope of\nour modeling framework and avoids the need for additional background on\nclimate system modeling.\nRegarding your second point on adopting a life-cycle perspective, we fully agree\nthat this is an important dimension of vehicle decarbonization. However, our\nstudy specifically focuses on operational decision-making at the vehicle use\nphase. These operational factors are unlikely to meaningfully impact upstream\nlife-cycle stages such as vehicle manufacturing, fuel production, or distribution.\nFurthermore, for most vehicle types—except zero-emission vehicles—the use\nphase typically accounts for the vast majority of life-cycle emissions (Wang et al.\n2022, Yeow et al. 2022, Iyer et al. 2023). Therefore, while incorporating a full life-\ncycle assessment might shift the primary focus of our research, it would likely\nnot change our key findings. As such, we believe that a full LCA is beyond the\nscope of the present study.\nReferences\n1. Iyer, R,K,, Kelly, J.C., and Elgowainy, A. 2023. Vehicle-cycle and life-cycle\nInternal\nanalysis of medium-duty and heavy-duty trucks in the United States.\nScience of the Total Environment. 891. 164093\n2. Yeow, L.W., Yan, Y., and Cheah, L. 2022. Life cycle greenhouse gas\nemissions of alternative fuels and powertrains for medium-duty trucks: A\nSingapore case study. Transportation Research Part D.105. 103258\n3. Wang, K., Zavaleta, V.G., Li, Y., Sarathy, M., and Abul-Manan, A. 2022. Life-\ncycle CO2 mitigation of China’s class-8 heavyduty trucks requires hybrid\nstrategies. One Earth. 5. 709-723\nInternal"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors provided a rebuttal and manuscript revision that are clarifying. It is now clear to me that the sample reference comparisons are used for context and background and are not used in computing future scenarios with increased truck-hailing (other than operational performance values of truck-hailing users, which are assumed to match those observed in the sample). The addition of extra sensitivity scenarios also helps to capture how results may vary if the assumptions used in the model are changed, making findings less sensitive to the specific assumptions made and addressing potential heterogeneity in treatment effects. Together these two factors improve my assessment of the manuscript substantially.\nI have a few remaining suggestions:\n1) The term \"median\" for the middle scenarios is misleading because these scenarios are not based on the median value of any dataset. A better term might be \"medium\" or \"middle\" or \"moderate\"\n2) For the statement \"all digitally matched trips are single origin-destination journeys, excluding multi-stop journeys that involve multiple origins and destinations,\" please clarify what is done with data on multi-stop journeys. If they are discarded, please indicate what percentage of the data is discarded and the degree to which systematically discarding those trips may potentially bias results. If they are included by breaking them up into sub-trips, each with a single origin and destination (as in SI Figure 3), please explain.\n3) When presenting the results breaking down contributions, if I am understanding correctly what you are showing, it is important to emphasize (e.g. in figure captions and surrounding text) that this decomposition of contributions is based on LMDI. LMDI is a systematic approach, but it is still only one of multiple ways that one could divide an effect into contributions from multiple sources when those sources have interaction effects, so interpreting these as contributions requires understanding what scheme is being used to decompose interacting effects.", "reviewer_3": "An important point of criticism from reviewer 2 was not addressed: to take into account the operational constraints for further consolidation. The authors responded by adding “median” scenarios but this is not addressing this critical issue effectively. It needs a causal relationship and explicit simulation of delivery schedules in the model.\nThis brings me to a crucial weak point in the structure of the article: it misses a clear description of the data and the methodology used, e.g. the fleet model is not explained. It is also not explained why this methodology was chosen over other types of freight demand models.\nThe article uses a relevant and large dataset with observed freight hailing services, but I question the validity of the methodology: it falls short in addressing structural shifts in freight transport demand over time. The method uses the observed sample from the base year to extrapolate increasing horizontal collaboration on a 40 year timescale. In such timespan freight demand will change significantly, in type of goods and origin destination patterns. This will affect the consolidation potential of freight hailing services.\nI agree with reviewer 1 that this analysis should be positioned as a ‘what-if’ analysis, and being so, it would make a better publication in a journal with such a scope."}, "author_response": "Response to Reviewers’ Comments for Manuscript NCOMMS-24-58987A-Z\nentitled “The potential role of truck-hailing and operational efficiency\nimprovement in decarbonizing China’s medium- and heavy-duty road freight\ntransport”\nWe are deeply grateful for the reviewers’ interest in our work and the supportive\nfeedback provided. The constructive comments and suggestions have been\ncarefully considered and have greatly helped in refining the manuscript. Please\nsee below the detailed responses to each point raised.\nReviewer #1 (Remarks to the Author):\nComment:\nThe authors provided a rebuttal and manuscript revision that are clarifying. It is\nnow clear to me that the sample reference comparisons are used for context and\nbackground and are not used in computing future scenarios with increased\ntruck-hailing (other than operational performance values of truck-hailing users,\nwhich are assumed to match those observed in the sample). The addition of\nextra sensitivity scenarios also helps to capture how results may vary if the\nassumptions used in the model are changed, making findings less sensitive to\nthe specific assumptions made and addressing potential heterogeneity in\ntreatment effects. Together these two factors improve my assessment of the\nmanuscript substantially.\nResponse:\nThank you for your positive feedback on our revised manuscript. We sincerely\nappreciate your comments, which have been invaluable in strengthening our\nstoryline, situating our work more effectively within the existing literature, and\nenhancing the robustness of our findings. We believe these revisions have\nfurther aligned the manuscript with the high standards ofNature\nCommunications.\nI have a few remaining suggestions:\nComment:\n1) The term \"median\" for the middle scenarios is misleading because these\nscenarios are not based on the median value of any dataset. A better term might\nbe \"medium\" or \"middle\" or \"moderate\"\nInternal\nResponse:\nWe appreciate this excellent point. As suggested, we have replaced the term\n\"median\" with \"medium\" throughout the manuscript. We have also adjusted\nFig.3 accordingly.\nComment:\n2) For the statement \"all digitally matched trips are single origin-destination\njourneys, excluding multi-stop journeys that involve multiple origins and\ndestinations,\" please clarify what is done with data on multi-stop journeys. If they\nare discarded, please indicate what percentage of the data is discarded and the\ndegree to which systematically discarding those trips may potentially bias\nresults. If they are included by breaking them up into sub-trips, each with a single\norigin and destination (as in SI Figure 3), please explain.\nResponse:\nThank you for raising this important question. The original dataset does not\ninclude any multi-stop journeys with multiple origins and destinations, and\ntherefore, no such data were discarded. This is because the online matching\nplatform from which the data are sourced does not allow multiple shipments to\nbe matched simultaneously. Once a carrier accepts a shipment, they are unable\nto receive new assignments until the current delivery is completed.\nFurthermore, as the data comes from the largest digital freight matching\nplatform in China, carriers typically select shipments that match their vehicle’s\ncapacity, leaving little room for additional cargo. The time-sensitive and\ncommercial nature of long-haul freight operations also prevents carriers from\ncombining shipments without explicit shipper authorization.\nIn response to your comments, we have refined the wording in the manuscript\n(“Truck-hailing sample” subsection in “Methods”):\n“All digitally matched trips are single origin-destination (OD) journeys, and do not\ninclude multi-stop journeys that involve multiple origins and destinations.”\nComment:\n3) When presenting the results breaking down contributions, if I am\nunderstanding correctly what you are showing, it is important to emphasize (e.g.\nin figure captions and surrounding text) that this decomposition of contributions\nis based on LMDI. LMDI is a systematic approach, but it is still only one of\nmultiple ways that one could divide an effect into contributions from multiple\nsources when those sources have interaction effects, so interpreting these as\ncontributions requires understanding what scheme is being used to decompose\ninteracting effects.\nResponse:\nThank you for this suggestion. We have updated the manuscript to clarify that the\ndecomposition analysis was conducted using the LMDI approach, as seen in the\ncaptions for Figures 5 and 6 and the paragraph following Figure 6 (see highlighted\nadditions)\nInternal\nReviewer #3 (Remarks to the Author):\nComment:\n1. An important point of criticism from reviewer 2 was not addressed: to take into\naccount the operational constraints for further consolidation. The authors\nresponded by adding “median” scenarios but this is not addressing this critical\nissue effectively. It needs a causal relationship and explicit simulation of delivery\nschedules in the model.\nResponse:\nWe sincerely thank the reviewer for this valuable suggestion. We fully agree that\noperational constraints are critical in practice, as they can fundamentally limit\nthe real-world potential for collaboration, efficiency gains, and subsequent\ndecarbonization.\nWhile we agree that incorporating delivery schedule into our modeling\nframework may improve model resolution, we believe that it will not likely\nchange our key findings, but could shift the primary focus of our research, as the\nmain objective of our study is to bridge methodological and data gaps by\ndeveloping a high-resolution model that captures macro level operational\nefficiency factors (like empty running) previously absent in road freight\ndecarbonization research.Additionally, incorporating delivery schedule into our\nmodeling approach also comes with several significant challenges, including:\n It still could not quantify and model most of the operational constraints\nthat are inherently non-technical in nature. These include business\npractices such as Just-in-time—which prioritizes low inventory levels over\nvehicle utilization—as well as legal and regulatory barriers, issues of trust\namong stakeholders, and concerns regardingthe sharing of sensitive\ninformation, etc., as noted in studies referenced by Reviewer 2 and other\nliterature (Basso et al., 2019; McKinnon, 2018; McKinnon and Ge, 2006).\n Much of the evidence on existing operational constraints comes from\nstudies in developed economies (such as Europe and North\nAmerica).Their applicability to China’s rapidly evolving logistics sector\nremains unclear, as suggested by Zhang et al. (2017).\n Modeling greater details, such as granular delivery schedules, would\nrequire a different type of data foundation and would likely expand the\nscope of this study considerably.\nTo address the reviewer’s valid concern that these practical limitations create\nuncertainty, the “Median” (now renamed as “Medium”) scenarios are explicitly\ndesigned to represent a more pragmatic operational efficiency outcome where\nreal-world operational constraints curtail theoretical potentials. We believe this\nscenario-based approach effectively captures the resulting uncertainty in the\nInternal\ndecarbonization pathway. At the same time, we also agree that the exploration of\nspecific operational constraints, as suggested by the reviewer, is a very valuable\nand necessary direction for future research, ideally enabled by more granular\ndata.\nComment:\n2. This brings me to a crucial weak point in the structure of the article: it misses a\nclear description of the data and the methodology used, e.g. the fleet model is\nnot explained. It is also not explained why this methodology was chosen over\nother types of freight demand models.\nResponse:\nThank you for raising this important point. While detailed information is provided\nin the Methods section, we recognize that the main text may not have sufficiently\nhighlighted our data and modeling approach, both of which constitute key\ncontributions of this study.\nA significant gap in current road freight decarbonization research involves the\nrole of logistics and operational efficiency in mitigating emissions—a shortfall\nlargely attributable to limitations in modeling capabilities and data availability.\nOur study helps address this gap by leveraging a large-scale, high-resolution\ntruck-hailing dataset and developing an enhanced vehicle fleet model that\nexplicitly incorporates key operational efficiency parameters.\nThe truck-hailing dataset integrates waybill records, geospatial information, and\nvehicle operational characteristics, enabling a detailed analysis of factors such\nas empty mileage and load factors. This unique empirical foundation supports\nthe development of a high-resolution transport-energy-emission model that\ncaptures variables previously overlooked by other models.\nMethodologically, our approach builds on established frameworks in transport\nenergy emission and integrated assessment modeling—the transport activity –\nmodal structure – energy intensity – fuel to carbon ratio approach—aligned with\nIPCC guidelines (see e.g., Schipper & Marie-Lilliu, 1999; Gucwa & Schafer, 2013;\nYeh et al., 2017; Argonne National Laboratory, 2018; Mulholland et al., 2018;\nSpeizer et al., 2024). Our primary innovation lies in enhancing this foundation\nwith a higher-resolution vehicle fleet model that can integrate operational\nvariables—such as the share of empty miles—using real-world operational data.\nIn direct response to your comment, we have expanded the descriptions of both\nthe data and the modeling approach in the main text and Methods section.\nPlease refer to the highlighted additions in the introduction (second to fourth last\nparagraphs), the “Vehicle operational performances in truck-hailing journeys”\nsubsection (first paragraph), and the “Methods” section for details.\nInternal\nIn the “Methods” section, the “Truck-hailing sample” subsection provides further\ndetails on the dataset, while “Enhanced vehicle fleet model” and\n“Decomposition of emission reduction by main contributing factors” elaborate\non the model structure. Specifically, Equations (2) and (3) illustrate how\noperational variables are incorporated, highlighting distinctions from\nconventional models.\nComment:\n3. The article uses a relevant and large dataset with observed freight hailing\nservices, but I question the validity of the methodology: it falls short in addressing\nstructural shifts in freight transport demand over time. The method uses the\nobserved sample from the base year to extrapolate increasing horizontal\ncollaboration on a 40 year timescale. In such timespan freight demand will\nchange significantly, in type of goods and origin destination patterns. This will\naffect the consolidation potential of freight hailing services.\nResponse:\nThank you very much for this insightful comment. You raise an excellent point\nabout the importance of structural shifts in goods type and origin-destination\npatterns; these demand-side factors are indeed fundamental in shaping future\nfreight activity.\nOur study focuses on the complementary supply-side perspective: namely, how\nfreight demand can be met by a national vehicle fleet operating with significantly\nimproved efficiency and lower emissions.As mentioned in our earlier response,\nour modeling framework is built on established methodologies in transport-\nrelated energy and climate change research, which have been widely utilized in\ntransport energy and decarbonization studies over long time horizons.\nWe completely agree that long-term structural changes in demand are crucial.\nHowever, for the specific purpose of our model—which is to isolate and analyze\nthe potential of supply-side levers like operational efficiency, vehicle technology,\nand fuel choice, among others—the direct impact of the demand structural\nshifts on these supply-side variables is less clear. Meanwhile, we believe that the\nuncertainties in fleet operation arising from these future demand changes can be\neffectively captured by our scenario-based approach, which tests a wide range\nof possible operational efficiency levels.\nTherefore, while integrating these important demand-side dynamics would\ncertainly enhance the model's resolution, we believe it might shift the research\nfocus away from our primary objective and is also a substantial endeavor that\nexceeds the current scope. That said, we greatly appreciate your suggestion and\nbelieve this provides a wonderful and important direction for future research. We\nwould be very interested in exploring these interactions in subsequent work.\nInternal\nComment:\n4. I agree with reviewer 1 that this analysis should be positioned as a ‘what-if’\nanalysis, and being so, it would make a better publication in a journal with such a\nscope.\nResponse:\nThank you for this thoughtful comment. We appreciate the concern regarding the\nspeculative nature of “what-if” analyses.\nAs we noted in our previous response to Reviewer 1, our study focuses on a\nspecific and underexplored aspect of decarbonization: the potential for\noperational efficiency gains in China's MHDT sector to reduce emissions.\nIn this context, we have chosen a scenario analysis—essentially a “what-if”\napproach. We believe this is an appropriate and well-established method for\nexploring plausible futures amidst significant uncertainty, much like the\ninfluential work of Sala et al. (2000), Rockström et al. (2009), and O’Neill et al.\n(2017). This methodology is particularly valuable for informing long-term policy\nand investment decisions when precise forecasts are impossible.\nIt's important to note that this approach is applied consistently through our\nanalysis. For instance, the projections for zero-emission vehicle adoption are\nalso \"what-if\" in nature, reflecting the current early stage of these technologies.\nUltimately, by utilizing an integrated scenario framework, we aim to provide a\nstructured approach to understanding how different combinations of operational\nimprovements and technological adoption can shape various decarbonization\npathways. We believe this offers valuable insights for stakeholders navigating\nthis complex transition.\nReferences\n1. Argonne National Laboratory (ARL). 2018. China vehicle fleet model:\nestimation of vehicle stocks, usage, emissions, and energy use.\nhttps://www.anl.gov/argonne-scientific-publications/pub/148761\n2. Gucwa, M., and Schafer, A. 2013. The impact of scale on energy intensity\nin freight transportation. Transport. Res. Part D-Transport. Environ. 23, 41-\n49\n3. Mulholland, E., Teter, J., Cazzola, P., McDonald, Z., Gallachóir, B. P. Ó. 2018.\nThe long haul towards decarbonising road freight – A global assessment to\n2050. Appl. Energy 216, 678-693.\n4. O’Neil, B.C., et al., 2017. The roads ahead: Narratives for shared\nsocioeconomic pathways describing world futures in the 21st century.\nGlobal Environmental Change. 42, 169-180\nInternal\n5. Rockström, J., Steffen, W., Noone, K.et al. 2009.A safe operating space\nfor humanity.Nature461, 472–475\n6. Sala, O.E.et al., 2000.Global Biodiversity Scenarios for the Year 2100.\nScience 287,1770-1774\n7. Schipper, Lee; Marie-Lilliu, Celine. 1999. Transportation and CO2\nemissions : flexing the link - a path for the World Bank (English).\nEnvironmental Department papers|no. 69. Climate change series\nWashington, D.C. : The World Bank.\nhttp://documents.worldbank.org/curated/en/826921468766156728\n8. Speizer, S., Fuhrman, J., Aldrete Lopez, L.et al.2024. Integrated\nassessment modeling of a zero-emissions global transportation\nsector.Nature Communications15, 4439.\n9. Yeh, S., Mishra, G., Fulton, L., Kyle, P., McCollum, D., Miller, J., Cazzola, P.,\n& Teter, J. 2017. Detailed assessment of global transport-energy models’\nstructures and projections. Transportation Research Part D: Transport and\nEnvironment, 55, 294-309.\nInternal"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors addressed my remaining concerns"}, "author_response": ""}]

Decision letter