Quantifying the effects of the 'Internet plus Ecology' framework on carbon sink in the digital age: a representative study of Ant Forest in China

Crawling the AOI in digital maps based on computer programming has become a practicable and popular method for GIS analysis (Zhou 2022, Yan et al 2002). We crawled the AOI data of the Ant Forest based on the AutoNavi map (figure 1), obtained multiple geographic information (including names, locations, and tree species). The detailed processes are provided in the supplementary material (section S2).

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The calculation of vegetation NPP in the CASA model (Potter et al 1993, figure 1) is mainly determined by two variables: absorbed photosynthetically active radiation (APAR) and light use efficiency () absorbed by vegetation:

$$\begin{equation}{\text{NP}}{{\text{P}}_{(x,t)}} = {\text{APA}}{{\text{R}}_{(x,t)}} \times {\varepsilon _{(x,t)}}\end{equation} \tag{ 1 }$$

where NPP_(x,t) is the total fixed NPP of pixel x in month t, and APAR_(x,t) and _(x,t) are equivalent.

2.2.1.  Calculation of APAR

APAR depends on the total solar radiation and the absorption ratio of vegetation to photosynthetically active radiation, which can be calculated by equation (2).

$$\begin{equation}{\text{APA}}{{\text{R}}_{(x,t)}}\, = \,{\text{SO}}{{\text{L}}_{(x,t)}}\, \times \,{\text{FPA}}{{\text{R}}_{(x,t)}}\, \times \,0.5\end{equation} \tag{ 2 }$$

where SOL_(x,t) is the total solar radiation (MJ m⁻²) of pixel x in month t, and FPAR_(x,t) is the fraction of photosynthetically active radiation absorbed by the canopy. The constant 0.5 represents the proportion of solar effective radiation (wavelength 0.4 – 0.7 μm) that can be used by vegetation.

Fraction of photosynthetically active radiation (FPAR) is related to vegetation type and vegetation coverage. The normalized differential vegetation index (NDVI) obtained from RS data can reflect the vegetation coverage in general (Potter et al 1993). Based on NDVI dynamics obtained using RS data (see section 2.3), we calculated the simple ratio (SR) of NDVI by equation (3),

$$\begin{equation}{\text{S}}{{\text{R}}_{(x,t)}}\, = \,\frac{{1 + {\text{NDV}}{{\text{I}}_{(x,t)}}}}{{1 - {\text{NDV}}{{\text{I}}_{(x,t)}}}}.\end{equation} \tag{ 3 }$$

Then, we calculated FPAR_(x,t) based on SR by equation (4).

$$\begin{align}{\text{FPA}}{{\text{R}}_{(x,t)}}\, & = \,\frac{{{\text{S}}{{\text{R}}_{(x,t)}} - {\text{S}}{{\text{R}}_{\min }}}}{{{\text{S}}{{\text{R}}_{\max }} - {\text{S}}{{\text{R}}_{\min }}}}\, \nonumber\\ &\quad \times \,({\text{FPA}}{{\text{R}}_{\max }} - {\text{FPA}}{{\text{R}}_{\min }})\, + \,{\text{FPA}}{{\text{R}}_{\min }}\end{align} \tag{ 4 }$$

where SR_max and SR_min represent the maximum and minimum ratios of SR, respectively, and FPAR_max and FPAR_min refer to the maximum value (0.95) and minimum value (0.001) of FPAR, respectively.

2.2.2.  Calculation of light energy conversion rate

The light energy conversion rate refers to the efficiency of vegetation to convert absorbed photosynthetically active radiation into organic carbon.

$$\begin{equation}{\varepsilon _{(x,t)}}\, = \,{\varepsilon _{\max }}\, \times \,{T_{\varepsilon 1(x,t)}}\, \times \,{T_{\varepsilon 2(x,t)}}\,\, \times \,\,{W_{\varepsilon (x,t)}}\end{equation} \tag{ 5 }$$

where _max is the maximum value of , and different vegetation has different values (see table S1 in the supplementary material). T _1(x,t), T _2(x,t) and W _(x,t) represent the stress effects of low temperature, high temperature and water, respectively.

$$\begin{equation}{T_{\varepsilon 1(x,t)}}\, = \,0.8\, + \,0.02\, \times \,{T_{{\text{opt}}(x)}}\, - \,0.0005\, \times \,{[{T_{{\text{opt}}(x)}}]^2}\end{equation} \tag{ 6 }$$

$$\begin{align}{T_{\varepsilon 2(x,t)}}\, & = \,\frac{{1.184}}{{1 + {e^{0.2 \times ({T_{{\text{opt}}(x)}} - 10 - {T_{(x,t)}})}}}}\, \nonumber\\ &\quad \times \,\frac{1}{{1 + {e^{0.3 \times ( - {T_{{\text{opt}}(x)}} - 10 - {T_{(x,t)}})}}}}\end{align} \tag{ 7 }$$

where T_opt(x) refers to the monthly average temperature in a certain year when NDVI is the largest in the region. When the value is less than or equal to −10 °C, T _1(x,t) takes 0. The plant growth reaches the fastest speed when NDVI reaches its maximum, and the temperature at this point is important for measuring plant growth.

$$\begin{equation}{W_{\varepsilon (x,t)}}\, = \,0.5\, + \,0.5\, \times \,\frac{{{E_{(x,t)}}}}{{{P_{(x,t)}}}}\end{equation} \tag{ 8 }$$

where E_(x,t) and P_(x,t) represent the actual and potential regional evapotranspiration, respectively, which are calculated from precipitation and sunshine hours (Zhu 2005).

We calculated the 2016–2020 vegetation NPP in each Ant Forest area obtained in section 2.1. Based on the CASA model, the vegetation NPP with a 250 m grid as the pixel and monthly as the time interval was simulated. The detailed data sources are provided in the supplementary material (section S3).

Through AOI searching, 588 pieces of forests named Ant Forest with a total area of 136 314 ha were collected, and they were distributed in seven provinces in China (figure 2). Specifically, the number of forest pieces per province (presented in descending order) in the Inner Mongolia, Gansu, Qinghai, Hebei, Sichuan, Shanxi, and Yunnan Provinces were 350, 132, 44, 40, 12, 7, and 3, respectively. With respect to the area of Ant Forest in each province, the ranked order changed to Inner Mongolia, Gansu, Qinghai, Shanxi, Sichuan, Hebei, and Yunnan Provinces, with area proportions of 47.36%, 36.95%, 8.00%, 6.60%, 0.54%, 0.53%, and 0.01%, respectively.

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Notably, Inner Mongolia was the greatest contributor in terms of both piece number and forest area, with approximately half of Ant Forest located in that province (figure 3). Moreover, we found that the provinces of Gansu and Qinghai were ranked 2nd and 3rd in terms of both piece number and forest area (figure 3). It should be noted that most areas of these three provinces (except for the four leagues in eastern Inner Mongolia) were found in the arid and semiarid region of Northwest China, which faces serious land degradation and a fragile ecological environment. In total, approximately 81.7% of the Ant Forest area was located in western Inner Mongolia, Gansu and Qinghai (figure 3), which highlighted the vital contributions of this ecological afforestation project to ecological restoration and environmental protection in Northwest China. From the perspective of different tree species of Ant Forest, mostly drought-resistant shrubs (e.g. Haloxylon ammodendron, Caragana korshinskii) were planted in these three provinces with arid and semiarid climates (figure 3).

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Hebei and Shanxi Provinces, located in North China and adjacent to Beijing, exhibited opposite characteristics in terms of piece number and total area of Ant Forest. Hebei ranked 4th among the seven provinces in piece number of Ant Forest, but this province ranked 6th in total area, indicating that the spatial distribution of Ant Forests in Hebei exhibited a fragmental structure. In contrast, Shanxi Province ranked 6th among the seven provinces in piece number of Ant Forest, while it ranked 4th in total area, indicating that many large-area forests were located in this province. Comparing the different species of Ant Forest (figure 3), it is notable that all individuals planted in Hebei were arbor trees, whereas all individuals planted in Shanxi were shrubs.

In Southwest China, Sichuan and Yunnan were two provinces distributed with little Ant Forest. In Sichuan Province, only Xiaojin County of Aba Prefecture was involved and had 12 forest blocks of Hippophae rhamnoides, with a total area of 740 ha (figure 3). In Yunnan Province, only Yunlong County of Dali Prefecture was involved and had three forest blocks of Pinus armandii and Picea asperata, with a total area of approximately 19 ha (figure 3). Planting trees in Southwest China should have positive effects on vegetation restoration and carbon storage in karst regions with vulnerable ecological environments (Liu et al 2015).

In general, a higher carbon sequestration was found in the forest areas of Northwest China according to the total NPP (figure 4). The annual average NPP in western Inner Mongolia, Gansu, and Qinghai provinces was 2.5 × 10¹⁰ gC, 2.3 × 10¹⁰ gC, and 1.4 × 10¹⁰ gC, respectively. The corresponding proportions of the total amount were 23.56%, 22.01%, and 13.42%, respectively. For the two provinces in North China, the annual average NPP in Shanxi was 1.7 × 10¹⁰ gC, which almost decupled the corresponding result of 1.9 × 10⁹ gC in Hebei. The NPP of Ant Forest in these two provinces accounted for approximately 18.15% of the total NPP. Similarly, for the two provinces in Southwest China, there was also a difference in order of magnitude between Sichuan (4.2 × 10⁹ gC) and Yunnan (1.3 × 10⁸ gC), which had a 4.04% total Ant Forest NPP amount. In addition, the annual average NPP in eastern Mongolia was 2.0 × 10¹⁰ gC, accounting for 18.82% of the total amount. The NPP per unit area is listed in table S2 in the Supplementary material. The prefecture-level city with the highest carbon sequestration was Qingyang in Gansu Province, with an annual average NPP of 1.8 × 10¹⁰ gC, followed by Xinzhou in Shanxi Province (1.6 × 10¹⁰ gC) and Haidong in Qinghai Province (1.4 × 10¹⁰ gC). Notably, these three cities ranked 3rd, 7th and 4th in terms of forest area nationwide (figure 3), indicating that they were not the cities with the largest Ant Forest area. This finding revealed that the spatial pattern of Ant Forest NPP was not exactly the same as the forest distribution, and meteorological variables such as temperature and precipitation might affect the NPP to some extent. Moreover, the vegetation species variation was another important factor that influenced the estimated NPP value.

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Therefore, the carbon sequestration effects of different vegetation species in the Ant Forest were further analyzed below (figure 5). In terms of the NPP amounts of different tree species, Hippophae rhamnoides (5.1 × 10¹⁰ gC yr⁻¹) had the greatest value, followed by Caragana korshinskii (4.3 × 10¹⁰ gC yr⁻¹). We found that the Hippophae rhamnoides forest contributed 48.38% of the total NPP with only 24.02% of the total Ant Forest area, and the Caragana korshinskii forest contributed 40.15% of the total NPP with only 26.16% of the total Ant Forest area. This indicated that the carbon sequestration effects of both Hippophae rhamnoides and Caragana korshinskii were prominent (figure 5). The NPP of Hippophae rhamnoides and Caragana korshinskii was 157 gC m⁻² yr⁻¹ and 120 gC m⁻² yr⁻¹, respectively; although both shrubs, theirs was close to or even greater than the NPP per unit area of Pinus sylvestris, a kind of arbor tree. We also found that Haloxylon ammodendron was the vegetation species with the highest proportion of planting area (31.15%), but its total carbon sequestration ranked only 5th among 11 vegetation species due to its low NPP value of 5.5 gC m⁻² yr⁻¹. It is possible that wind prevention and sand fixation were the main afforestation goals of this type of shrub, which usually has a well-developed root system but lower leaf area. Among all species, Picea asperata had the highest carbon sequestration capacity (with an NPP value of 780 gC m⁻² yr⁻¹), which was approximately more than 142 times the value of Haloxylon amodendron and approximately more than 3.7 times the average level of all 11 vegetation species. However, due to having the smallest proportion of planted area (0.014%), the two arbor forests of Picea asperata and Pinus armandii had the lowest total NPP.

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In all seven provinces, the total NPP of Ant Forest showed an increasing trend during the period from 2016 to 2020 (figure 6), indicating that this 'Internet + voluntary tree planting' project contributed to carbon sequestration and sinks in China. However, influenced by the interannual variation in meteorological factors, the NPP did not exhibit a monotonic increasing change. Except for a common increase found in all seven provinces from 2019 to 2020, the interannual changes in forest NPP between different regions varied; a detailed description is provided in the supplementary material (section S4). Using linear regression, the variation rates of forest NPP in different regions were assessed (figure 6). A rapidly-increasing trend was found in Northwest China, with a range of 1.00 × 10⁹–4.94 × 10⁹ gC yr⁻¹. The other four provinces, listed in descending order, were Shanxi, Sichuan, Hebei, and Yunnan, with variation rates of 1.41 × 10⁹ gC yr⁻¹, 6.46 × 10⁸ gC yr⁻¹, 2.53 × 10⁷ gC yr⁻¹, and 1.58 × 10⁷ gC yr⁻¹, respectively. In addition, in eastern Inner Mongolia, which is located in Northeast China, a linear increasing rate of forest NPP of 6.49 × 10⁸ gC yr⁻¹ was found, which was slightly lower than the variation speed in western Inner Mongolia.

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With calculations based on different vegetation species, we found that the variation rate of shrub NPP was slightly higher than that of arbor NPP (figure 7). These differences (supplementary materials section S5) might be related to the spatial variations in precipitation, temperature, terrain, and elevation throughout different regions in China.

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Since the launch of Ant Forest in 2016, it has attracted nearly 500 million OGUs and is, to date, the world's largest online public environmental platform (Chen et al 2020a). Aside from the forest carbon sink, the contribution to carbon neutrality of Ant Forest also includes carbon emission reduction through carbon-light behavioral changes of OGUs motivated to collect green-energy points.

A total of 304 valid questionnaires from OGUs aged between 18 and 60 years old in 35 provinces of China were analyzed. The results showed that 35.8% of the surveyed OGUs selected environmental awareness as the most important motivation (figure 8(a)), implying that OGUs had a strong wish to improve the environment as a public good through real tree planting. This finding was consistent with Chen et al (2020a), which indicated that both environmental awareness and social motivation had significant positive promotional effects on Ant Forest OGUs' online immersion and that environmental awareness was higher than social motivation. Moreover, 20.0% and 16.0% of the surveyed OGUs selected the game interaction and the immersive experience of tracking the planted trees as the major attraction, respectively (figure 8(a)). Wang and Yao (2020) also demonstrated that the gamification design elements of Ant Forest had a positive influence on OGUs' satisfaction.

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To further analyze the low-carbon behaviors for collecting green points of Ant Forest users, the surveyed samples were divided into two groups: college students (aged between 18 and 21 years old) and other people. We found that green commuting, reducing use of paper and plastics, and reducing trips were the three major categories of OGUs' low-carbon behaviors (figure 8(c)). In terms of the specific low-carbon action in the green commuting category, walking, subway, and public transport were the most commonly used pathways to collect the green points among all surveyed users (figure 8(c)). For college student OGUs, sharing bikes was another main action for reducing carbon emissions related to green commuting (figure 8(c)).

Of the 304 surveyed samples, the average accumulating carbon footprint in personal carbon accounts was 99 658 g (figure 8(b)). Calculating based on the average accumulating carbon footprint of 99 658 gC/person, the estimated carbon emission reduction of the 500 million Ant Forest users was approximately 4.98 × 10¹³ gC in total. The results of these analyses emphasized the significant impact of Ant Forest on incentivizing individuals' low-carbon behaviors, which is vital for achieving carbon neutrality in China.

As a burgeoning 'Internet plus Ecology' platform, the Ant Forest has been proven to be an effective strategy to increase public engagement in climate change and enhance the contribution of individuals to carbon neutrality through the rapid advancement of digital technology. On the one hand, it aims to encourage users to adopt a low-carbon lifestyle, which would reduce carbon emissions from the perspective of carbon sources. On the other hand, it promotes ecological afforestation by planting trees, which could increase carbon sequestration from the perspective of carbon sinks. Therefore, the Ant Forest should have innovativeness and can be a model for Chinese carbon neutrality as well as combating climate change on a global scale. Ant Forest won the Champion of the Earth Award (the United Nations' highest environmental honor) and the United Nations Lighthouse Award (the highest honor in the world for Combating Climate Change) in 2019, reflecting the high level of recognition from the international community achieved by this Chinese 'Internet + voluntary tree planting' digital green solution.

The Ant Forest, associated with its 'digital technology + gamification design + public participation' framework, is a significant product for enhancing the efforts of individuals to tackle climate change. It is a globally representative case with ecological, environmental and economic benefits. We found that by August 2021, the total area of identified Ant Forest was 136 314 ha, which was equal to the area of 1.9 Singapore; the accumulating NPP of the Ant Forest from 2016 to 2020 was 5.31 × 10¹¹ gC, which was approximately 1.03 times the carbon emissions of Singapore in 2019. Moreover, it should be noted that the Ant Forest also played an important role in Chinese poverty alleviation through ecological protection. For instance, Hippophae rhamnoides is a type of economic tree that is usually used to produce drinks due to its abundant vitamin C and medicinal value. In the future, if we could collect more data, further work assessing the comprehensive benefit of economic forests and ecological forests separately should be performed.

This paper is an innovative quantitative study aiming to identify and estimate the distribution of Ant Forest and its effect on carbon sinks based on the independent data source from RS rather than the macroscopic panel data published by Alibaba; it fills the gap of geographically discussing this world's first 'Internet plus ecology' project. Some uncertainties might exist in the NPP estimation taking 2016–2020 as the analysis period by using the boundary of Ant Forest crawling from the AutoNavi platform in 2021. However, it could be reasonably explained by the assumption that the area with planted trees was almost completely converted from barren land, which usually had no vegetation cover (section S6 in the supplementary material). Our results showed that there was an increasing trend of NPP variations in the Ant Forest area during 2016–2020, which provided a solid scientific foundation for the effectiveness of this ecological afforestation and its carbon sequestration. If more data can be collected, the parameters relevant to meteorological data used in the CASA model, and the consideration regarding forest age and the growth changes of different vegetation species, may be improved in the future. However, this limitation did not affect the most novel aim of this study: to quantitatively estimate the spatial pattern of Ant Forest and its carbon sequestration effect.

More than 90% of the Ant Forest were located in the arid and semi-arid areas, which might exacerbate more severe water shortages after planting trees. Another improvement would be to simulate on the effects on the water cycle along with the carbon cycle to estimate this ecological afforestation more comprehensively.