Promoting green transportation through changing behaviors with low-carbon-travel function of digital maps

The study addresses how to promote green, low-carbon urban transportation in China by leveraging digital tools to influence travel behavior. Against the backdrop of rising transport-related greenhouse gas emissions globally and in China, the research aims to: (a) assess public awareness, preferences, and willingness to adopt green transportation modes; (b) evaluate the role of digital map/navigation applications in facilitating green mobility; and (c) examine whether incentive mechanisms such as carbon inclusion policies increase willingness to use green transport. The authors hypothesize: H1: Urban travelers increasingly understand, accept, and prefer green and low-carbon transportation; H2: Digital tools (e.g., mobile navigation apps) provide innovative, engaging options that increase willingness to use green transport; H3: Effective incentive mechanisms (e.g., carbon inclusion) further enhance willingness for green travel. The work is important for meeting China’s dual carbon goals (peak by 2030, neutrality by 2060) and for informing practical pathways to reduce transport emissions through behavior change and digitalization.

The review notes that transport emissions rose from 4.8 to 8.5 Gt CO2e (1990–2018), with road transport contributing ~73%. Technology shifts alone face challenges; demand-side strategies (altered lifestyles, travel behavior, housing patterns, teleworking) are needed. China’s car ownership surged (400% increase, 2009–2021), underscoring urgency for alternatives to private cars. Policies in China (Overall Action Plan for Green Life, 2019; Green Transportation Action Plan, 2020) target higher shares of public, walking, and cycling, with city-level goals (e.g., Shenzhen targeting 81% green transport by 2025). Literature indicates when public transport scale exceeds a threshold, its relationship with emissions turns from positive to negative; green transport also reduces pollutants like PM2.5. Digitalization enables access to information and can nudge behavior; digital solutions are estimated to reduce 15–20% of global emissions, potentially over 12 Gt CO2e by 2030 via smart systems. In China, digital maps (e.g., Tencent Maps) have integrated a green transport portal (covering >180 cities and >200 million users), supporting real-time transit, ticketing via ride codes, and multimodal planning, suggesting promise for demand-side behavioral shifts.

Design comprised two components: (1) a nationwide questionnaire survey of green transportation behaviors and attitudes; and (2) a Shenzhen case study estimating carbon emission reductions attributable to digital map–enabled green transit usage. - Questionnaire: Random distribution by Tencent Horizon to mobile internet users across 31 provinces/autonomous regions/municipalities, approximating CNNIC netizen structure. Obtained 7369 valid responses (exceeding sample size for 95% confidence, 3% error). Demographics: 51% male, 49% female; 70% aged ≤39. Shenzhen netizens were the most active respondents. Survey captured mode preferences, factors influencing mode choice, willingness to shift to greener modes, and attitudes toward digital map features, incentives, and carbon-inclusive mechanisms. - Shenzhen case study: Evaluated whether mobile navigation apps assist low-carbon transport. Focus city: Shenzhen (population 17.66 million), where Tencent Maps’ Green Transportation Portal integrates metro, buses, walking, cycling, and shared bikes. Emission reductions were calculated only for users taking subway and bus trips by scanning Tencent Map codes, using voluntary emission reduction methodology aligned with UN CDM and China’s certified voluntary programs. Formula: ER = BE – PE, with BE = ΣAD_i × EF_i (baseline emissions from most probable replaced modes; EF urban traffic emission factor) and PE = ΣAD_i × EF_i (project emissions from metro/bus). Data: Tencent Green Transportation Big Data. For metro (2019–2021): total mileage 25,841,178,916 person-km; EFbaseline (urban traffic) 0.0812 kgCO2/P-km; EFmetro 0.0345 kgCO2/P-km. For electric bus (Jan 2021–May 2022): cumulative mileage 1,000,568,937 person-km; EFbus 0.0543 kgCO2/P-km. Emissions converted to tCO2e by dividing by 1000. Calculations yielded metro ER ≈ 1,206,783 tCO2e (BE ≈ 2,098,304; PE ≈ 891,521) over 2019–2021, and bus ER ≈ 26,915 tCO2e (BE ≈ 81,246; PE ≈ 54,330) over 17 months.

- Finding 1: Modal preferences indicate rising public transport usage. Public transit preferred by 50.6% of respondents; walking 43.0%; electric bikes 39.5%; bicycles 37.1%; motorcycles 10.7%. Car users 32.0%. Combining transit with walking/biking yields >50% using public transport. In Shenzhen subset: 93% use public transit for daily commuting; 31% walk; 20% cycle; private car 10%; taxi/ride-hailing 16%. - Finding 2: Key decision factors are convenience, safety, and weather. Proportions considered: convenience 48.7%; safety 34.3%; weather 29.6%; comfort 28.7%; low cost 27.6%; low carbon/environmental protection 27.3%; short time 25.9%; randomness 24.5%. - Finding 3: Strong willingness to support national green transportation action. 67.2% would switch from carbon-intensive to greener modes; 26% likely to switch; 6% unlikely; 1.4% would not. Among unwilling, 58% cited commute time. Factors encouraging green routes from digital maps when acceptable distance/time: acceptable commute time 51.8%; low carbon/environmental protection 47.6%; fitness 35.6%; low fares/cost efficiency 33.5%; high punctuality/less congestion 33.3%; flexibility 32.5%; safety 23.3%. - Finding 4: High acceptance of mobile apps that support green transport. Over 80% favor green-transport–oriented map features. Specific attractive functions: real-time public transit info 46.2%; dynamic real-time traffic updates 45.3%; recording calories for biking/walking 43.5%. - Finding 5: Carbon inclusion incentives are preferred, especially transactional ones. 67.7% want personal carbon reductions/credits in apps that can be traded or exchanged for cash/coupons; 57.0% support personal carbon accounts; 48.9% support donating credits to philanthropic projects; 37.2% favor green certificates; 18.5% ranking lists. Among supporters, 68% are ≤39. - Finding 6: Tencent Maps’ green transportation portal increases willingness and awareness. Among Tencent Maps users: 34.5% noticed and frequently used the portal; 30.9% noticed occasionally; 16.7% noticed but never used; 17.9% did not notice/use. Over 90% of users of the module agreed it made trips easier, saved time, improved fitness, reduced expense, and strengthened green transport awareness; only 0.5% disagreed that it contributes to dual-carbon goals. - Finding 7: High satisfaction with metro and bus facilities in Shenzhen and Beijing. Very/somewhat satisfied: metro—Shenzhen 89%, Beijing 87%; bus—Shenzhen 85%, Beijing 83% (bus satisfaction lower than metro, likely due to time). - Emission reductions (Shenzhen): Metro ER ≈ 1,206,783 tCO2e (2019–2021) via Tencent Map green transport platform; Electric bus ER ≈ 26,915 tCO2e (Jan 2021–May 2022).

Findings support the hypotheses that Chinese urban travelers accept and prefer green transportation (H1), that digital tools increase willingness to use it (H2), and that incentive mechanisms enhance this willingness (H3). The survey shows majority preference for public transport and strong acceptance of green app features; transactional carbon credits and personal carbon accounting are particularly motivating, especially among younger users. Case results from Shenzhen demonstrate substantial emission reductions attributable to metro and electric bus trips facilitated by digital map tools. The discussion situates these outcomes within broader policy and planning contexts: expanding and improving public transport (rail and BRT) reduces energy use and emissions; convenience and travel time dominate user decisions, so digital features that reduce wait/transfer times and enable one-stop trip planning are critical to shifting behavior. Despite COVID-19 temporarily depressing public transport use relative to non-epidemic periods, digital mechanisms and carbon-inclusive initiatives (e.g., Beijing’s MaaS program with ~1 million registrants, ~100,000 tons cumulative emission reductions, and 24,500 tons traded) illustrate scalable pathways. The authors argue digitalization has become a primary means to promote green transport, complementing infrastructure expansion and city-level targets, and that standardized methodologies for quantifying and transacting individual emission reductions can further catalyze adoption.

The study contributes empirical evidence—via a nationally representative netizen survey and a Shenzhen case study—that public acceptance of green transportation in China is high, that digital map/navigation tools significantly encourage adoption and improve user experience, and that carbon inclusion incentives further enhance willingness to choose low-carbon modes. Quantified impacts in Shenzhen indicate sizable CO2 reductions through digital map–enabled metro and electric bus use. The authors recommend coordinated action among government, social organizations, enterprises, and digital technology firms to develop and implement standardized emission reduction methodologies and incentives for low-carbon lifestyles, especially in transportation. Future research should partner with digital technology companies to refine inclusive carbon rules, build a national carbon-inclusion standard and trading system, and develop validation and verification methods for green transportation in more pilot cities to enable broader replication and to deepen public engagement and benefits from green mobility.

- Timing: The survey was conducted during the COVID-19 period, when public transport usage was atypically lower and walking/taxi/private car shares higher than non-epidemic periods, potentially reducing observed positive impacts of digital tools. - Scope of emission calculations: Shenzhen case estimates only included subway and electric bus trips taken via Tencent Map code scanning, excluding other modes (e.g., walking, cycling) and trips not initiated/scanned through the app. - Geographic focus for case: Emission reduction quantification was limited to Shenzhen; generalizability to other cities requires further pilots. - Sample frame: The questionnaire targeted mobile netizens via Tencent Horizon; while statistically representative of Chinese netizens, it may not fully represent non-internet users.