Inferring Carbon Emissions from Human Mobility Data

Inferring Carbon Emissions from Human Mobility Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Physical Sciences - Article Inferring Carbon Emissions from Human Mobility Data Xin Lu, Mengning Wang, Xiaoling Zhang, Suoyi Tan, Qing Zhang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3657266/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate identification, quantification, and continual monitoring of carbon emissions constitute pivotal elements for proactive climate interventions. Conventional methodologies like direct measurement, including point-source assessments or remote sensing, often face challenges related to high costs or limited accuracy. Especially in low- and middle-income nations experiencing escalating emissions, carbon monitoring heavily relies on the administrative capabilities of local governments, which frequently lack adequate monitoring infrastructures. Addressing this predicament, our study introduces a computational framework to forecast CO 2 emissions by leveraging comprehensive observable human activity data from third-party sources. Our findings elucidate a robust correlation between multi-origin CO 2 emissions and human mobility ( r = 0.89). Notably, machine learning models adeptly predict these emissions by integrating characteristics extracted from temporally aggregated, anonymized mobility networks (R 2 ≈1.0). We demonstrate that the model effectively captures the notable reduction in CO 2 emissions during the COVID-19 lockdown, with both human mobility and CO 2 emissions in China decreasing by 56.97% and 32.45%, respectively. The prediction accuracy remains high for countries with varying social economic development, such as the U.S., Italy and Mexico. This study presents an inexpensive, real-time, and robust method of quantifying CO 2 emissions on a large scale with high precision, and it could facilitate tailored CO 2 emission reduction strategies, grounded in robust scientific evidence derived from the dynamics of human mobility. Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction Scientific community and society/Social sciences/Climate change/Projection and prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Carbon dioxide (CO 2 ) emissions have been driving long-term climate change 1-3 , posing huge threats to human health 4 , economic activities 5 , and agriculture 6 . While top-down approaches to CO 2 mitigation, such as national policies and SDGs, encounter delays due to lock-ins of energy infrastructure and resistance from stakeholders, bottom-up approaches centered on behavioral shifts in human activities offer promising avenues for comprehensive decarbonization 7 as transportation-derived CO 2 emissions emerged as the predominant source of greenhouse gases by 2017 8 . The international community has set ambitious targets under the United Nation’s Pairs Agreement to bring global emissions to net-zero by the mid-21st century. This commitment necessitates each signatory country to articulate its emissions targets for the forthcoming decades. Accurate and effective carbon monitoring is a central requirement of the Pairs Agreement, anchored on periodic assessments of countries’ commitments and advancements at five-year intervals. The agreement mandates the improvement and innovation of carbon monitoring techniques, a constant theme at the United Nations Framework Convention on Climate Change’s (UNFCCC) annual Conference of the Parties 9 . Considerable efforts have been made to accurately quantify CO 2 emissions using different methodologies. Countries such as Japan, the United States, and China have utilized satellites to monitor CO 2 concentration. However, financial limitations often prevent many low- and middle-income countries (LMICs) from implementing similar practices. Alternatively, scientists infer emissions indirectly using methods like mass balance approach 10 , emission factors-based measurements 11 , life cycle carbon emissions assessment 12 , and input-output analysis 13 . Despite their utility, these methods, however, require significant time, resources, human effort, and remain susceptible to unexpected errors. For example, life-cycle analysis, a comprehensive and intricate method, assesses emissions throughout the entire life cycle of a product, process, or service, encompassing emissions from production, utilization, and disposal phases, but there are challenges in determining responsibility for emissions. Another avenue involves leveraging mathematical or machine learning models to predict CO 2 emissions from various sources, such as marine fleets 14 , intercity freight 15 , commercial activities 16 , cement industry 17 , and residential 18 sectors. These models often integrate annually updated factors such as GDP 19,20 , energy usage 21 , and population 22 . However, their primary emphasis on yearly forecasting of CO 2 emissions might constrain the timeliness for effective governmental oversight and policy formulation. As we navigate toward the global emission target, the effort to real-time monitoring and prompt trend detection of CO 2 emissions become evident, underscoring the need for nuanced strategies attuned to both temporal and spatial considerations. In this study, we propose a computational framework (Extended Data Fig. 1 and Methods) to predict CO 2 emissions by harnessing insights from human mobility patterns. Human mobility, with its abundance of geolocated details, provides a valuable lens through which to understand various social phenomena. It also has been demonstrated to be pivotal in urban planning 23 , traffic forecasting 24 , epidemic modeling 25,26 , disaster analysis 27 , and social-economic development estimation 28 . Drawing from China’s national level Cellular Signaling Data (CSD) as a proxy for human mobility, the unparalleled resolution and dependable quality of the human mobility data enable us to analyze and predict CO 2 emissions from an independent and novel perspective. Moreover, leveraging human mobility data to infer CO 2 emissions can greatly enhance the efficiency of CO 2 emissions data collection, analysis, and reporting. The developed computational framework could automate this process, reduce human errors and time costs, and improve the efficiency of carbon emissions management. Results Spatio-temporal synchronization between population mobility and multi-source CO 2 emissions High synchronization exists between population mobility and CO 2 emissions. The correlation between daily national multi-source CO 2 emissions, encompassing emissions from seven sources (power, industry, residential consumption, ground transportation, domestic aviation, international aviation, and international shipping), and the total population mobility was Pearson’s r =0.89 during the study period from January 1 to February 29, 2020 (Fig. 1a). Notably, the first two months of 2020 witnessed human mobility being quickly and drastically changed by the COVID-19 lockdowns. Despite these perturbations, the temporal synchronization between CO 2 emissions and population mobility remained stable. Daily intercity population movement increased from an average of 53.53 million to a peak of 68.74 million during the Lunar New Year travel season (starting on January 10), subsequently dropping by 71.30% due to travel restrictions. This decline was followed by a gradual recovery as restrictions were lifted. Correspondingly, multi-source CO 2 emissions, which averaged 356.52 million kgC/d from January 1 to January 17, experienced a sharp drop to their lowest levels of 209.64 million kgC/d on February 13, marking a 41.20% decrease. Emissions then embarked on a recovery with intermittent fluctuations. This pattern of variation was also mirrored in the CO 2 emissions specifically associated with domestic aviation and ground transportation (Fig. 1b, c). Furthermore, the spatial distribution of multi-source CO 2 emissions in China is of significant heterogeneity and unevenly distributed among cities (Fig. 1d). The analysis of regions sharing the same latitude reveals that cities on the east coast exhibit substantially higher CO 2 emissions compared to their western counterparts, Longitudinally, CO 2 emissions are notably more concentrated in the northern cities. A parallel trend is observed in the distribution of population mobility across cities. These observations demonstrate a spatial consistency between multi-source CO 2 emissions and population mobility, with Pearson’s r = 0.49. These findings confirm that changes in intercity population movements and CO 2 emissions exhibit temporal and spatial consistency. Furthermore, the discrepancies in CO 2 emissions between cities correspond with the varying connectivity of intercity mobility. Consequently, the large-scale population mobility data illustrated in this study is deemed a reliable predictor of CO 2 emissions. Correlations between mobility network features and CO 2 emissions In this investigation, we focused on examining the complex connection between human mobility and CO 2 emissions by analyzing network topology. To achieve this, we created temporal directed mobility networks using both unweighted and weighted approaches (see Methods). From these networks, we extracted different node characteristics to access the activity features of cities and global characteristics for each network snapshot, allowing us to gain a comprehensive understanding of population movement over various time periods. Detailed descriptions of network features analyzed in this study can be found in Supplementary Table 1, which are further categorized into unweighted and weighted node and global features. Our analysis revealed positive correlations between node features and CO 2 emissions, indicating cities with higher transportation accessibility and extensive connections to other cities tend to have increased CO 2 emissions (Fig. 1e). Furthermore, all global features demonstrated strong correlations with CO 2 emissions, with the majority of |r | values surpassing 0.80. Notably, the number of edges (NE) had a strong positive correlation with CO 2 emissions, reaching r =0.86. In contrast, the Strength Assortativity coefficient (SA) had a pronounced negative correlation, quantified at . Further insights into the correlation analysis can be found in Extended Data Table 1, Supplementary Figs. 1 and 2, and Extended Data Fig 2. More surprisingly, node characteristics derived from unweighted mobility networks showed a stronger correlation with CO 2 emissions when compared to their weighted counterparts (Fig. 1e). For example, the correlation values for Inward Closeness Centrality (ICC), Eigenvector Centrality (EC), and Betweenness Centrality (BC) were 0.50, 0.46, 0.38, respectively. In contrast, the correlation of Weighted Inward Closeness Centrality (WICC), Weighted Eigenvector Centrality (WEC), and Weighted Betweenness Centrality (WBC) were 0.31, 0.07, 0.27. This suggests that the network topology features, even without the detailed edge flow information, play a significant role in understanding spatio-temporal variation in CO 2 emissions. Predicting CO 2 emissions with mobility network features In this section, we combined the selected network features from the preceding step with the corresponding geographic and demographical information to predict CO 2 emissions. The integration yields a complete dataset with a sample size of 60 (days) × 366 (cities) =21,960. Subsequently, we employed eight distinct machine learning models and two different data split strategies to predict CO 2 emissions. Split Strategy 1 involved randomly partitioned all the 21,960 samples, whereas split Strategy 2 entailed partitioning based on days, ensuring that, samples from the same day were simultaneously allocated to either the training or test set (Extended Data Fig. 3). Among the models tested, the LGBM demonstrated the best performance, achieving R 2 value near 1.0 in predicting multi-source CO 2 emissions under both data split strategies. The performance of all other models varied slightly depending on the data split strategies, with Strategy 1 generally performing better (Extended Data Table 2). Fig. 2 shows the agreement between the predicted and observed CO 2 in mainland China during the turbulent period of Chinese Spring Festival Travel Rush and strict lockdown measures, indicating that LGBM accurately models the relationship between human mobility and CO 2 emissions. Despite the substantial fluctuations in both human mobility and CO 2 emissions throughout the study period, the predictions remained satisfactory (R 2 ≥0.98). For split Strategy 1, LGBM outperforms all the rest models with R 2 =1.00, 0.99, 0.98 for predicting CO 2 from multi-source, domestic aviation, and ground transportation, respectively; and for split Strategy 2, LGBM remains the best performed model with R 2 =1, 0.97 and 0.94 for the three types of CO 2 . XGB runs next to LGBM as the second-best model in both scenarios, with slightly decreased R 2 (0%~1%) and higher RMSE (7%~17%) and MAE (2%~18%). The significant superiority of LGBM and XGB indicates that, the gradient boosting frameworks for ensemble learning are powerful in predicting spatio-temporal carbon emission with high precision, such that there is a great potential of extrapolating the approach to other settings with either missing observable emission data for some places, or for forecasting future trend of emission with human activity data. Feature importance and robustness analysis The importance of geographic and demographic information in predicting CO 2 emissions was confirmed by SHapley Additive exPlanations (SHAP) values 29 (Methods). This analysis identified latitude (LAT), longitude (LON), and population (POP) as the three most crucial features (Fig. 3a, b, and Supplementary Fig. 3), which consistent with previous studies 30,31 . Notably, the substantial difference in winter temperature across different latitudes in China contributes to divergent heating demands. The northern region, characterized by its heavy industrial base, requires more heating compared to the southern region, which is predominantly developed in light industry. This climatic and industrial disparity significantly influences the regional CO 2 emissions. Connectivity and population flow, as represented by network features such as WDAC, WICC, OCC and IS, were found to be crucial for predicting CO 2 emissions. To further investigate the contribution of network information to the prediction, an ablation experiment was executed. A null model was built using LGBM trained solely on latitude, longitude and population. The model’s predictions were repeated 200 times with independent random splits to mitigate potential biases from data splitting (Supplementary Figs. 4, 5). Notably, when network features were incorporated, there was a marked improvement in prediction accuracy compared to the null model. For multi-source CO 2 emissions, the R 2 value increased from 0.93 to 0.98. Additionally, an increase of over 60% was observed across other evaluation settings, such as RMSE and MAE (Fig. 3c, Extended Data Table 3). To further evaluated the prediction performance, we conducted analysis that involved gradually extending the study period and increasing the time span of training data. Specifically, at each timestep, the model was trained using all historical data, meaning that for prediction the outcome on the n th day, the training set consisted of data accumulated from the preceding n -1 days. The results demonstrate that an increase in the quantity of historical data led to significant improvements in all three prediction performance metrics (Fig. 3d-e). Notably, there was a distinct turning point observed on the seventh day, indicating that even with just one week’s worth of historical data, the method could generate predictions with satisfactory accuracy. This resilience was evident despite substantial fluctuations in population mobility. Moreover, we conducted sensitivity analyses by considering segmentation and scaling in the training and test sets, reaffirming the robustness of our model and ensuring accurate predictions across diverse conditions (Extended Data Fig. 4). Generalization and extrapolation of the method to Italy, the U.S. and Mexico To validate the generalizability and practicality of the proposed method, we utilized open-source mobility data from Italy 32 , the U.S. 33 , and Mexico 34 as a basis for predicting their respective CO 2 emissions (Fig. 4a-c). Surprisingly, despite the distinct socio-economic landscapes of these countries, our method consistently demonstrated commendable forecasting accuracy across all the three countries. In the case of Italy, outflows of each province were normalized on a daily basis, leading to the exclusion of weighted network features in our predictive model. Despite this constraint, we achieved an impressive R 2 value of 0.91. Similarly, our method demonstrated success in predicting CO 2 emissions for the U.S. and Mexico, yielding R 2 values of 0.96 and 0.99, respectively (Fig. 4d-f, and in Supplementary Fig. 6). These results demonstrate the generalizability and efficacy of the computational framework across diverse countries, highlighting its considerable potential for monitoring and forecasting carbon emission in LMICs where reliable emission measurements are often deficient. Delineating CO 2 emissions in city clusters through human mobility Given the notable carbon footprint of city areas, it is imperative to establish specialized carbon mitigation units, tailored to city clusters, for effective monitoring and reduction of carbon emissions. The previous finding of close association between CO 2 emissions and human mobility (Figs. 1 and 2), suggests the latter as a promising objective means of delineating city clusters. Therefore, we utilized Louvain algorithm 35 (Methods) to detect communities in mobility networks during the period unaffected by Chunyun and COVID-19 lockdowns (Supplementary Table 2). The study period coincided with the run-up to the annual Chunyun mass migration and COVID-19 epidemic, which was divided into four distinct periods: normal times (January 1-9), Chunyun (January 10-23), stringent travel restrictions (January 24 - February 10), and recovery times (February 11-29). During the normal times, significant geographical disparities in CO 2 emissions across city clusters were observed. Notably, the Beijing-Tianjin-Hebei (62.75 million kgC/d), the Northwest (Ordos) (60.24 million kgC/d), and the Yangtze River Delta (49.71 million kgC/d) were identified as leading emitters, primarily due to their dense population and industrial development activities (Fig. 5a-c). In addition, our findings indicate that polycentric city clusters are associated with higher CO 2 emission efficiency compared to city clusters with a concentric structure centered around a single unban core. For example, the Northwest (Changji) city cluster records emissions at 15.32 million kgC/d, significantly lower than 60.24 million kgC/d observed in the Northwest (Ordos). A detailed examination of CO 2 emissions across different time periods reveals a dispersion of emissions throughout China. During the Chunyun period, characterized by high variability in mobility patterns, emissions were more dispersed throughout China (Fig. 5d). Stringent travel restrictions imposed during the COVID-19 pandemic substantially reduced CO 2 emissions in all city clusters (Fig. 5e). In the subsequent recovery period, strengthened local interactions within the mobility network resulted in lower CO 2 emissions compared to normal levels (Fig. 5f). These investigations, which delve into the examination of CO 2 emissions within city clusters, have significant implications for designing of customized CO 2 emissions policies. Such an approach can contribute to effective governance of CO 2 emissions at an aggregated level. Discussion This study underscores the potential of utilizing human mobility data to make accurate CO 2 emissions prediction. Common transportation modes such as airplanes, high-speed trains, and long-distance buses play a crucial role in shaping intercity connections, and it follows that network features derived from human mobility networks demonstrate significant potential in predicting CO 2 emissions from domestic aviation and ground transportation. More notably, these same network features have shown considerable efficacy in forecasting multi-source CO 2 emissions across seven distinct carbon sources. Also, this study offers an inexpensive method for predicting CO 2 emissions, which could be particularly valuable for implementation in economically disadvantaged nations. Our approach leverages existing data, unlike carbon monitoring satellites that have restricted coverage in LMICs, making scaling across countries simple and nearly cost-free. Particular noteworthy is the resilience of our method in face of fluctuating human mobility patterns observed during the study period, ranging from the Chunyun —the largest annual human migration in the world 36 —to the exceptional calm of COVID-19 lockdowns. Despite these variances, the approach consistently yielded reliable predictions. It is imperative to develop innovative and cost-effective solutions for monitoring CO 2 emissions, as they play a vital role in attaining emissions targets set forth by the Paris Agreement. Our research employed community detection methods of human mobility networks to categorize cities into city clusters. This analysis revealed considerable variations in CO 2 across different clusters, highlighting the need to pinpoint specific characteristics of carbon-intensive cities. City clusters can serve as effective units for monitoring and coordinating efforts to reduce CO 2 emissions, enabling collaborative planning to achieve emission reduction goals. It has the potential to lower the cost of CO 2 emissions mitigation, which is particularly valuable for LMICs where detailed mapping of city clusters is limited. Despite the continuous growth of cities, there are steps that city planners can take into mitigate carbon emissions. These measures include promoting the adoption of cleaner sources of industrial and domestic energy, implementing energy-efficient ground transportation technologies, and improving rail transportation systems as a viable to domestic aviation. However, it is often perceived that these solutions are unpredictable and costly. Therefore, it is crucial to implement scalable policies that can effectively achieve these objectives and significantly reduce carbon emissions from city clusters. Our research findings align with the previous studies that have established a correlation between CO 2 emissions and various factors such as socio-economic development 37 , energy consumption 38,39 , and nighttime stable light 40 . Additionally, it is important to note that an accurate and efficient carbon monitoring approach plays a pivotal role in fostering international agreements and collaborations on climate change, as demonstrated by the Paris Agreement. Our proposed solutions not only deepen our understanding of the relationship between human activity and CO 2 emissions but also provide practical tools and strategies for effectively managing carbon emissions in the face of ongoing socio-economic and environmental challenges. These efforts are crucial for achieving development goals in a post-climate change era. Declarations Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this paper. Data Availability The aggregated mobility matrices from China analyzed in this study are available on GitHub (https://github.com/wangmengning/MobilityForCO2Prediction). To facilitate the review process, an encrypted version of the data is accessible, requiring the password XGUTZH to decompress the data. The weighted daily origin-destination matrices for Italy are available at https://data.humdata.org/dataset/covid-19-mobility-italy. The multiscale dynamic human mobility flow dataset for the U.S. can be accessed at https://github.com/GeoDS/COVID19USFlows. The daily intermunicipal travel networks of Mexico are available at https://osf.io/42xqz/. The Global Gridded Daily CO 2 Emissions Dataset (GRACED) is available at https://carbonmonitor-graced.com/. Code availability All the code to reproduce the results are available on GitHub (https://github.com/wangmengning/MobilityForCO2Prediction). To facilitate the review process, the same code XGUTZH can be used to download and extract the code files. Acknowledgments We great appreciate Professor Maribel Hernández Rosales and Guillermo de Anda-Jáuregui for providing the mobility data in Mexico and for further data clarifications. This work was supported by the National Nature Science Foundation of China (72025405, 72088101, 72001211), the Innovation Team Project of Colleges in Guangdong Province (2020KCXTD040), and the Hunan Science and Technology Plan Project (2020TP1013). Inclusion & Ethics statement We declare that all individuals who contributed significantly to this work have been appropriately acknowledged. The accountability of authorship strictly complies with relevant national and international regulations and guidelines regarding research ethics, such as those listed by the Committee on Publication Ethics (COPE) and the International Council of Medical Journal Editors (ICMJE). Author Contributions X.L. conceived the research. M.N.W., X.L.Z., S.Y.T., and Z.Q. performed the research. M.N.W. and Z.Q. collected and cleaned the data. M.N.W., Z.Q., B.S., M.S.C., and S.H.G. analyzed the data. M.N.W., X.L.Z. wrote the paper. X.L., M.N.W., X.L.Z., S.Y.T., D.W., M.O., M.J.L., Z.L. and X.H.C revised the paper. Declaration of interests All authors declare no competing interests. Additional information Peer review information. Reprints and permissions information is available at http://www.nature.com/reprints. References Stocker, T. F. et al. Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2013). Eskander, S. M. S. U. & Fankhauser, S. Reduction in greenhouse gas emissions from national climate legislation. Nature Climate Change 10 , 750-756 (2020). Jones, M. W. et al. National contributions to climate change due to historical emissions of carbon dioxide, methane, and nitrous oxide since 1850. Scientific Data 10 , 155 (2023). Costello, A. et al. Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission. The Lancet 373 , 1693-1733 (2009). Hsiang, S. et al. Estimating economic damage from climate change in the United States. Science 356 , 1362-1369 (2017). Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nature Climate Change 4 , 287-291 (2014). Pacala, S. & Socolow, R. Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies. Science 305 , 968-972 (2004). Shirley, C. & Gecan, R. Emissions of Carbon Dioxide in the Transportation Sector (Congressional Budget Office, 2022). United Nations Climate Change Secretariat. Draft Implementation Plan with respect to Net-Zero Pledges of non-State actors and Integrity Matters (United Nations Climate Change Secretariat, 2023). Cambaliza, M. O. L. et al. Assessment of uncertainties of an aircraft-based mass balance approach for quantifying urban greenhouse gas emissions. Atmospheric Chemistry and Physics 14 , 9029-9050 (2014). Shen, L. et al. Factory-level measurements on CO2 emission factors of cement production in China. Renewable and Sustainable Energy Reviews 34 , 337-349 (2014). Chau, C. K., Leung, T. M. & Ng, W. Y. A review on Life Cycle Assessment, Life Cycle Energy Assessment and Life Cycle Carbon Emissions Assessment on buildings. Applied Energy 143 , 395-413 (2015). Jiang, T., Yu, Y., Jahanger, A. & Balsalobre-Lorente, D. Structural emissions reduction of China's power and heating industry under the goal of “double carbon”: A perspective from input-output analysis. Sustainable Production and Consumption 31 , 346-356 (2022). Cao, Y., Yin, K., Li, X. & Zhai, C. Forecasting CO2 emissions from Chinese marine fleets using multivariable trend interaction grey model. Applied Soft Computing 104 , 107220 (2021). Lu, I. J., Lewis, C. & Lin, S. J. The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector. Energy Policy 37 , 2952-2961 (2009). Wen, L. & Yuan, X. Forecasting CO2 emissions in China’s commercial department, through BP neural network based on random forest and PSO. Science of The Total Environment 718 , 137194 (2020). Ofosu-Adarkwa, J., Xie, N. & Javed, S. A. Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion. Renewable and Sustainable Energy Reviews 130 , 109945 (2020). Wen, L. & Cao, Y. Influencing factors analysis and forecasting of residential energy-related CO2 emissions utilizing optimized support vector machine. Journal of Cleaner Production 250 , 119492 (2020). Sharma, S. S. Determinants of carbon dioxide emissions: Empirical evidence from 69 countries. Applied Energy 88 , 376-382 (2011). Magazzino, C., Mele, M. & Schneider, N. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions. Renewable Energy 167 , 99-115 (2021). Khan, M. K., Teng, J.-Z., Khan, M. I. & Khan, M. O. Impact of globalization, economic factors and energy consumption on CO2 emissions in Pakistan. Science of The Total Environment 688 , 424-436 (2019). Ribeiro, H. V., Rybski, D. & Kropp, J. P. Effects of changing population or density on urban carbon dioxide emissions. Nature Communications 10 , 3204 (2019). Bassolas, A. et al. Hierarchical organization of urban mobility and its connection with city livability. Nature Communications 10 , 4817 (2019). Çolak, S., Lima, A. & González, M. C. Understanding congested travel in urban areas. Nature Communications 7 , 10793 (2016). Tan, S. et al. Mobility in China, 2020: a tale of four phases. National Science Review 8 , nwab148 (2021). Jia, J. S. et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 582 , 389-394 (2020). Lu, X., Bengtsson, L. & Holme, P. Predictability of population displacement after the 2010 Haiti earthquake. Proceedings of the National Academy of Sciences 109 , 11576-11581 (2012). Pappalardo, L., Pedreschi, D., Smoreda, Z. & Giannotti, F. Using big data to study the link between human mobility and socio-economic development . In Proceedings of the 2015 IEEE International Conference on Big Data (Big Data) 871-878 (IEEE, 2015). Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence 2 , 56-67 (2020). Dietz, T. & Rosa, E. A. Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences 94 , 175-179 (1997). Zheng, J. et al. Regional development and carbon emissions in China. Energy Economics 81 , 25-36 (2019). Pepe, E. et al. COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data 7 , 230 (2020). Kang, Y. et al. Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic. Scientific Data 7 , 390 (2020). Fontanelli, O. et al. Intermunicipal travel networks of Mexico during the COVID-19 pandemic. Scientific Reports 13 , 8566 (2023). Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008 , P10008 (2008). Cripps, K. & Wang, S. World’s largest annual human migration now underway in China (CNN Travel, 2019). Soytas, U., Sari, R. & Ewing, B. T. Energy consumption, income, and carbon emissions in the United States. Ecological Economics 62 , 482-489 (2007). Zhang, L., Pang, J., Chen, X. & Lu, Z. Carbon emissions, energy consumption and economic growth: Evidence from the agricultural sector of China's main grain-producing areas. Science of The Total Environment 665 , 1017-1025 (2019). Canadell, J. G. et al. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proceedings of the National Academy of Sciences 104 , 18866-18870 (2007). Yang, D., Luan, W., Qiao, L. & Pratama, M. Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery. Applied Energy 268 , 114696 (2020). Data and Methods Mobility data China . The dataset was provided by one of China’s largest national mobile operators, China Unicom, which had 318 million active users as of 2019 41 . The mobility data used in this study was derived from Cellular Signaling Data (CSD), and was aggregated into city-level mobility matrices on a daily basis. To exclude population flow resulting from brief stays in a city, users who spent less than 30 minutes in a city were filtered out. The dataset encompasses the period from January 1, 2020, to February 29, 2020, a critical period during the COVID-19 pandemic outbreak. Italy . The dataset captures aggregated movements between different Italian provinces, utilizing location data derived from a large-scale dataset of anonymously shared positions of about 170,000 de-identified smartphone users and a total of about 200 million data points at the sub-national scale 32 . The mobility data is recorded in the form of OD (origin-destination) matrix, which was normalized by row, with each entry ( i , j ) in the matrix representing the proportion of user movements to province j out of those from province i . The dataset spans from January 18, 2020, to June 26, 2020. The U.S. The dataset was sourced from anonymous mobile phone user visits to various locations provided by SafeGraph 33 . The mobility patterns were aggregated at two geographical scales: county and state. The reliability of this dataset was confirmed through a high correlation with openly available data sources. The dataset covers the period from January 1, 2020, to February 29, 2020. Mexico . The dataset is a collection of human mobility networks which depict travel patterns between municipalities in Mexico 34 . These networks were constructed based on geolocation data obtained from mobile devices. In the network, each municipality was represented as a node, and the weight of an edge ( i , j ) corresponded to the total number of observed trips from municipality i to municipality j , and was normalized by the varying number of mobile devices recorded on a given day. The dataset covers the period from January 1, 2020, to December 31, 2020. Details of the mobility datasets can be found in Supplementary Information. CO 2 emissions data The CO 2 emissions data comes from the Global Gridded Daily CO 2 Emissions Dataset (GRACED) 42 . GRACED offers a daily spatial resolution of 0.1°×0.1°, encompassing CO 2 emissions from seven sectors: power, industry, residential consumption, ground transportation, domestic aviation, international aviation, and international shipping. GRACED is derived by Carbon Monitor national-level daily emissions, which are also allocated to finer-grid cells using spatial patterns from the Global Carbon Grid (GID) and the Emission Database for Global Atmospheric Research (EDGAR), as well as sub-national proxy based on TROPOMI NO2 retrievals. Rasterizing the global map at a resolution of 0.1°×0.1° results in matrix of 1800×3600 grids. GRACED is recorded in the form of an 1800×3600 matrix, with each element representing CO 2 emissions that correspond to a grid in the rasterized map. Network construction Computational framework Based on the large-scale, third-party, and non-intrusive mobility data, our study introduces a comprehensive computational framework for predicting CO 2 emissions. This framework initiates with the acquisition of mobility data and the subsequent construction of mobility networks. These networks facilitate the extraction of predictor features. Next, network features ( X ) are integrated with the additional data, such as population and geographical coordinates ( X’ ), to fine-tune a machine learning model on the training data. The fine-tuned model is then capable of being applied to predict the CO 2 emissions ( Y ) in scenarios where specific time-location pair of CO 2 emissions data are unavailable. To perform the prediction, we divided the dataset into training and testing sets using two different strategies ( Strategy 1 and Strategy 2, Extended Data Fig. 2). We then applied eight supervised learning algorithms (Multiple Linear Regression (MLR), Polynomial Regression (PR), Least Absolute Shrinkage and Selection Operator (LASSO) 43 , Ridge Regression (RR) 44 , K-Nearest Neighbors Regression (KNNR) 45 , Support Vector Regression (SVR) 46 , Artificial Neural Network (ANN), Random Forest (RF) 47 , eXtreme Gradient Boosting (XGB) 48 , Light Gradient Boosting Machine (LGBM) 49 ) to make a crosswise comparison of their ability to predict CO 2 emissions. In conducting these experiments, stratified 10-fold cross-validation was employed on the training set using the above eight prediction models. To optimize the training process, we employed z-score normalization during data preprocessing. This step involved normalizing the labels of the training set prior to model training and then de-normalizing the prediction results for final output. The effectiveness of the out-of-sample forecast capability was then evaluated using three key metrics: coefficient of determination (R 2 ), root mean square error (RMSE) and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) values SHAP is an additive method to overhaul each feature contribution to the prediction results in the learning model 50 . SHAP actually attributes the output value to the shapely value of each feature 51 . The output value is given as Louvain algorithm The Louvain algorithm utilizes multi-level modularity optimization to extract the community structure of large networks, and consists of two iteratively repeated phases. In the first phase, the algorithm computes a partition of the given network through modularity optimization. This phase stops when the modularity reaches a local maximum, indicating that no individual movement can improve it. In the second phase, the same community is folded to create a new weighted network. The first phase is then reapplied to the resulting weighted network and iterated. The algorithm aims to maximize modularity, defined as follows: References 41 China Unicom. Number of China Unicom mobile subscriptions from 2012 to 2022 (in millions). Statista https://www.statista.com/statistics/233968/number-of-china-unicom-mobile-subscriptions/ (2023) 42 Dou, X. et al. Near-real-time global gridded daily CO2 emissions. The Innovation 3 , 100182 (2022). 43 Tibshirani, R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58 , 267-288 (1996). 44 Hoerl, A. E. & Kennard, R. W. Ridge Regression: Applications to Nonorthogonal Problems. Technometrics 12 , 69-82 (1970). 45 Abeywickrama, T., Cheema, M. A. & Taniar, D. k-nearest neighbors on road networks: a journey in experimentation and in-memory implementation. Proc. VLDB Endow. 9 , 492-503 (2016). 46 Cortes, C. & Vapnik, V. Support-vector networks. Machine Learning 20 , 273-297 (1995). 47 Breiman, L. Random Forests. Machine Learning 45 , 5-32 (2001). 48 Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System . In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785-794 (ACM, 2016). 49 Ke, G. et al. LightGBM: a highly efficient gradient boosting decision tree . In Advances in neural information processing systems (NIPS) 3149-3157 (Curran Associates Inc., 2017). 50 Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions . In Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS) 4768-4777 (Curran Associates Inc., 2017). 51 Shapley, L. S. A Value for n-Person Games. Contributions to the Theory of Games 2 , 307-317 (1953). 52 Sundararajan, M. & Najmi, A. The Many Shapley Values for Model Explanation . In Proceedings of the 37th International Conference on Machine Learning(ICLM) 9269-9278 (PMLR, 2020). Additional Declarations There is NO Competing Interest. Supplementary Files Extendeddata.docx Extended Data Figs. 1-3, Extended Data Tables. 1-3 SupplementaryInformation.docx Inferring Carbon Emissions from Human Mobility Data (SI) Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3657266","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":265236382,"identity":"17e254f0-bc8a-4408-b4bb-6eb34e42cc74","order_by":0,"name":"Xin 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05:45:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3657266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3657266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49243194,"identity":"228830f0-c495-46bf-b702-b82acbd35caa","added_by":"auto","created_at":"2024-01-05 18:35:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1197214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the human mobility and CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea-c.\u003c/strong\u003e Changes in intercity mobility and CO\u003csub\u003e2\u003c/sub\u003e emissions of multi-source, domestic aviation, and ground transportation from January 1 to February 29, 2020. \u003cstrong\u003ed. \u003c/strong\u003eThe geographic distribution of average daily multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions and intercity mobility between January 1 and February 29, 2020. The upper map represents the average CO\u003csub\u003e2\u003c/sub\u003e emissions at 0.1°×0.1° gridded level, where darker color indicates higher CO\u003csub\u003e2\u003c/sub\u003e emissions. The lower map represents the daily average intercity mobility network during the study period with links weighted over 500. \u003cstrong\u003ee.\u003c/strong\u003e Heatmap of correlations between network features and multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions. The color bar represents the correlations, where a darker purple indicates a more significant positive correlation, and a darker green represents a stronger negative correlation. The abbreviations for network features: IDC, In-degree Centrality; ODC, Out-degree Centrality; ICC, Inward Closeness Centrality; OCC, Outward Closeness Centrality; EC, Eigenvector Centrality; BC, Betweenness Centrality; PR, PageRank; C, Clustering coefficient; IS, In Strength; OS, Out Strength; WICC, Weighted Inward Closeness Centrality; WOCC, Weighted Outward Closeness Centrality; WEC, Weighted Eigenvector Centrality; WBC, Weighted Betweenness Centrality; WC, Weighted Clustering coefficient; AR, Attraction Rate; TR, Transfer Rate; NE, Number of Edges; D, Density; AC, Average Clustering coefficient; T, Transitivity; DA, Degree Assortativity coefficient; TW, Total Weights; AWC, Average Weighted Clustering coefficient; SA, Strength Assortativity coefficient.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3657266/v1/36c9257e9ee6cdf6dd8c3bd5.jpg"},{"id":49243192,"identity":"6dd0c83c-29f7-4237-b85a-2b61c730d5e3","added_by":"auto","created_at":"2024-01-05 18:35:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":237361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelation between observed CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions and predicted CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions in 366 cities over 60 days.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea-c.\u003c/strong\u003e Out-of-sample observed versus predicted values for CO\u003csub\u003e2\u003c/sub\u003e emissions of multi-source, domestic aviation, and ground transportation with \u003cem\u003eStrategy\u003c/em\u003e 1. The horizontal and vertical histograms show the marginal distributions of predicted and observed CO\u003csub\u003e2\u003c/sub\u003e emissions, respectively. Predictions during the abrupt period of stringent travel restrictions (January 24-February 10), during which the human mobility and CO\u003csub\u003e2\u003c/sub\u003e emissions varied dramatically, are highlighted with red color.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3657266/v1/dafe2ff42606b049170bda1a.jpg"},{"id":49243196,"identity":"0dfdc5ab-03bb-42f9-b6c4-ecc0d82fc8f5","added_by":"auto","created_at":"2024-01-05 18:35:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":508598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of model performance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, b.\u003c/strong\u003e Summary of SHAP values for predicting multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions using \u003cem\u003eStrategy \u003c/em\u003e1 and\u003cem\u003e Strategy \u003c/em\u003e2. SHAP values can be used to determine the significance of specific features and how they impact the prediction. The feature importance is ranked by summing the magnitudes of the SHAP values, with the highest ranking features at the top. Each point represents a sample, and the color corresponds to the feature’s value. The impact on the model’s output is illustrated on the x-axis. LAT, LON, and POP are abbreviations of the city’s latitude, longitude, and population, respectively. \u003cstrong\u003ec.\u003c/strong\u003e Improved predictive performance is achieved through the integration of network information when compared to the null model. In terms of R\u003csup\u003e2\u003c/sup\u003e, improvement refers to the growth rate, whereas for RMSE and MAE, improvement represents the decline rate. \u003cstrong\u003ed. \u003c/strong\u003eThe changes in model evaluation metrics R\u003csup\u003e2\u003c/sup\u003e, RMSE, and MAE. These models are trained with all historical data, in other words, the training data of the \u003cem\u003en\u003c/em\u003eth day is the data of the previous \u003cem\u003en\u003c/em\u003e-1 days.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3657266/v1/b88b2cd3305892048030a06f.jpg"},{"id":49243591,"identity":"c8463c40-b863-4bfe-923e-8fcffa80695b","added_by":"auto","created_at":"2024-01-05 18:43:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":428615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-source CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions prediction performance in other countries. a-c,\u003c/strong\u003e Depiction of mobility network of Italy, the U.S., and Mexico at the province, county, state resolution, respectively. For visual simplicity, only the links with weights greater than 1000 are shown for the U.S. mobility network. \u003cstrong\u003ed-f, \u003c/strong\u003eComparisons of the predicted and observed CO\u003csub\u003e2\u003c/sub\u003e emissions of the three countries. The inset displays the state level prediction performance in the U.S.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3657266/v1/f75ce6129f79981807a911dd.jpg"},{"id":49243195,"identity":"15c11360-fc72-487a-a57e-aa13c70dbc66","added_by":"auto","created_at":"2024-01-05 18:35:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4551180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacteristics of human mobility and CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e emissions in city\u003c/strong\u003e \u003cstrong\u003eclusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e Municipal average daily mobility network and associated CO\u003csub\u003e2\u003c/sub\u003e emissions in normal times. Each bubble corresponds to a city, with its size indicating the amount of CO\u003csub\u003e2\u003c/sub\u003e emissions. Nodes of the same color denote city clusters identified by the Louvain algorithm. The link indicates the directed average daily number of trips between the connected nodes. The network is visualized with links weighted over 1000. The inset displays the multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions of city clusters, which is a sum of emissions from cities in the same city clusters. \u003cstrong\u003eb. \u003c/strong\u003eThe community structure of all city clusters during normal times in Chinese mainland. \u003cstrong\u003ec-f.\u003c/strong\u003e Distributions of CO\u003csub\u003e2\u003c/sub\u003e emissions in city clusters across periods.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3657266/v1/839e4dcdcbb468b5ac1d7057.jpg"},{"id":64187998,"identity":"ce634284-2365-4393-9738-488ef6312bf4","added_by":"auto","created_at":"2024-09-09 16:50:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7779784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3657266/v1/91278c46-5f12-4dd0-9456-ed12a41b04b2.pdf"},{"id":49243198,"identity":"a1c611ea-52a0-43eb-be0e-46ed5659deb5","added_by":"auto","created_at":"2024-01-05 18:35:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5920467,"visible":true,"origin":"","legend":"Extended Data Figs. 1-3, Extended Data Tables. 1-3","description":"","filename":"Extendeddata.docx","url":"https://assets-eu.researchsquare.com/files/rs-3657266/v1/68fe8c207dd2cb4f8f9b1fce.docx"},{"id":49243592,"identity":"d6a3486f-2d65-40d3-80f8-39bc5c8a8e86","added_by":"auto","created_at":"2024-01-05 18:43:55","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2786728,"visible":true,"origin":"","legend":"Inferring Carbon Emissions from Human Mobility Data (SI)","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3657266/v1/896886bf1021036b0491c8cb.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Inferring Carbon Emissions from Human Mobility Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCarbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions have been driving long-term climate change \u003csup\u003e1-3\u003c/sup\u003e, posing huge threats to human health \u003csup\u003e4\u003c/sup\u003e, economic activities \u003csup\u003e5\u003c/sup\u003e, and agriculture \u003csup\u003e6\u003c/sup\u003e. While top-down approaches to CO\u003csub\u003e2\u003c/sub\u003e mitigation, such as national policies and SDGs, encounter delays due to lock-ins of energy infrastructure and resistance from stakeholders, bottom-up approaches centered on behavioral shifts in human activities offer promising avenues for comprehensive decarbonization \u003csup\u003e7\u003c/sup\u003e as transportation-derived CO\u003csub\u003e2\u003c/sub\u003e emissions emerged as the predominant source of greenhouse gases by 2017 \u003csup\u003e8\u003c/sup\u003e. The international community has set ambitious targets under the United Nation\u0026rsquo;s Pairs Agreement to bring global emissions to net-zero by the mid-21st century. This commitment necessitates each signatory country to articulate its emissions targets for the forthcoming decades. Accurate and effective carbon monitoring is a central requirement of the Pairs Agreement, anchored on periodic assessments of countries\u0026rsquo; commitments and advancements at five-year intervals. The agreement mandates the improvement and innovation of carbon monitoring techniques, a constant theme at the United Nations Framework Convention on Climate Change\u0026rsquo;s (UNFCCC) annual Conference of the Parties \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eConsiderable efforts have been made to accurately quantify CO\u003csub\u003e2\u003c/sub\u003e emissions using different methodologies. Countries such as Japan, the United States, and China have utilized satellites to monitor CO\u003csub\u003e2\u003c/sub\u003e concentration. However, financial limitations often prevent many low- and middle-income countries (LMICs) from implementing similar practices. Alternatively, scientists infer emissions indirectly using methods like mass balance approach \u003csup\u003e10\u003c/sup\u003e, emission factors-based measurements \u003csup\u003e11\u003c/sup\u003e, life cycle carbon emissions assessment \u003csup\u003e12\u003c/sup\u003e, and input-output analysis \u003csup\u003e13\u003c/sup\u003e. Despite their utility, these methods, however, require significant time, resources, human effort, and remain susceptible to unexpected errors. For example, life-cycle analysis, a comprehensive and intricate method, assesses emissions throughout the entire life cycle of a product, process, or service, encompassing emissions from production, utilization, and disposal phases, but there are challenges in determining responsibility for emissions. Another avenue involves leveraging mathematical or machine learning models to predict CO\u003csub\u003e2\u003c/sub\u003e emissions from various sources, such as marine fleets \u003csup\u003e14\u003c/sup\u003e, intercity freight \u003csup\u003e15\u003c/sup\u003e, commercial activities \u003csup\u003e16\u003c/sup\u003e, cement industry \u003csup\u003e17\u003c/sup\u003e, and residential \u003csup\u003e18\u003c/sup\u003e sectors. These models often integrate annually updated factors such as GDP \u003csup\u003e19,20\u003c/sup\u003e, energy usage \u003csup\u003e21\u003c/sup\u003e, and population \u003csup\u003e22\u003c/sup\u003e. However, their primary emphasis on yearly forecasting of CO\u003csub\u003e2\u003c/sub\u003e emissions might constrain the timeliness for effective governmental oversight and policy formulation. As we navigate toward the global emission target, the effort to real-time monitoring and prompt trend detection of CO\u003csub\u003e2\u003c/sub\u003e emissions become evident, underscoring the need for nuanced strategies attuned to both temporal and spatial considerations.\u003c/p\u003e\n\u003cp\u003eIn this study, we propose a computational framework (Extended Data Fig. 1 and Methods) to predict CO\u003csub\u003e2\u003c/sub\u003e emissions by harnessing insights from human mobility patterns. Human mobility, with its abundance of geolocated details, provides a valuable lens through which to understand various social phenomena. It also has been demonstrated to be pivotal in urban planning \u003csup\u003e23\u003c/sup\u003e, traffic forecasting \u003csup\u003e24\u003c/sup\u003e, epidemic modeling \u003csup\u003e25,26\u003c/sup\u003e, disaster analysis \u003csup\u003e27\u003c/sup\u003e, and social-economic development estimation \u003csup\u003e28\u003c/sup\u003e. Drawing from China\u0026rsquo;s national level Cellular Signaling Data (CSD) as a proxy for human mobility, the unparalleled resolution and dependable quality of the human mobility data enable us to analyze and predict CO\u003csub\u003e2\u003c/sub\u003e emissions from an independent and novel perspective. Moreover, leveraging human mobility data to infer CO\u003csub\u003e2\u003c/sub\u003e emissions can greatly enhance the efficiency of CO\u003csub\u003e2\u003c/sub\u003e emissions data collection, analysis, and reporting. The developed computational framework could automate this process, reduce human errors and time costs, and improve the efficiency of carbon emissions management.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSpatio-temporal synchronization between population mobility and multi-source CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh synchronization exists between population mobility and CO\u003csub\u003e2\u003c/sub\u003e emissions. The correlation between daily national multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions, encompassing emissions from seven sources (power, industry, residential consumption, ground transportation, domestic aviation, international aviation, and international shipping), and the total population mobility was Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e=0.89 during the study period from January 1 to February 29, 2020 (Fig. 1a). Notably, the first two months of 2020 witnessed human mobility being quickly and drastically changed by the COVID-19 lockdowns. Despite these perturbations, the temporal synchronization between CO\u003csub\u003e2\u003c/sub\u003e emissions and population mobility remained stable. Daily intercity population movement increased from an average of 53.53 million to a peak of 68.74 million during the Lunar New Year travel season (starting on January 10), subsequently dropping by 71.30% due to travel restrictions. This decline was followed by a gradual recovery as restrictions were lifted. Correspondingly, multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions, which averaged 356.52 million kgC/d from January 1 to January 17, experienced a sharp drop to their lowest levels of 209.64 million kgC/d on February 13, marking a 41.20% decrease. Emissions then embarked on a recovery with intermittent fluctuations. This pattern of variation was also mirrored in the CO\u003csub\u003e2\u003c/sub\u003e emissions specifically associated with domestic aviation and ground transportation (Fig. 1b, c).\u003c/p\u003e\n\u003cp\u003eFurthermore, the spatial distribution of multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions in China is of significant heterogeneity and unevenly distributed among cities (Fig. 1d). The analysis of regions sharing the same latitude reveals that cities on the east coast exhibit substantially higher CO\u003csub\u003e2\u003c/sub\u003e emissions compared to their western counterparts, Longitudinally, CO\u003csub\u003e2\u003c/sub\u003e emissions are notably more concentrated in the northern cities. A parallel trend is observed in the distribution of population mobility across cities. These observations demonstrate a spatial consistency between multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions and population mobility, with Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e= 0.49.\u003c/p\u003e\n\u003cp\u003eThese findings confirm that changes in intercity population movements and CO\u003csub\u003e2\u003c/sub\u003e emissions exhibit temporal and spatial consistency. Furthermore, the discrepancies in CO\u003csub\u003e2\u003c/sub\u003e emissions between cities correspond with the varying connectivity of intercity mobility. Consequently, the large-scale population mobility data illustrated in this study is deemed a reliable predictor of CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations between mobility network features and CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this investigation, we focused on examining the complex connection between human mobility and CO\u003csub\u003e2\u003c/sub\u003e emissions by analyzing network topology. To achieve this, we created temporal directed mobility networks using both unweighted and weighted approaches (see Methods). From these networks, we extracted different node characteristics to access the activity features of cities and global characteristics for each network snapshot, allowing us to gain a comprehensive understanding of population movement over various time periods. Detailed descriptions of network features analyzed in this study can be found in Supplementary Table 1, which are further categorized into unweighted and weighted node and global features.\u003c/p\u003e\n\u003cp\u003eOur analysis revealed positive correlations between node features and CO\u003csub\u003e2\u003c/sub\u003e emissions, indicating cities with higher transportation accessibility and extensive connections to other cities tend to have increased CO\u003csub\u003e2\u003c/sub\u003e emissions (Fig. 1e). Furthermore, all global features demonstrated strong correlations with CO\u003csub\u003e2\u003c/sub\u003e emissions, with the majority of \u003cem\u003e|r\u003c/em\u003e| values surpassing 0.80. Notably, the number of edges (NE) had a strong positive correlation with CO\u003csub\u003e2\u003c/sub\u003e emissions, reaching \u003cem\u003er\u003c/em\u003e=0.86. In contrast, the Strength Assortativity coefficient (SA) had a pronounced negative correlation, quantified at \u003cimg src=\"data:image/png;base64,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\" height=\"25\" width=\"78\"\u003e. Further insights into the correlation analysis can be found in Extended Data Table 1, Supplementary Figs. 1 and 2, and Extended Data Fig 2.\u003c/p\u003e\n\u003cp\u003eMore surprisingly, node characteristics derived from unweighted mobility networks showed a stronger correlation with CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions when compared to their weighted counterparts (Fig. 1e). For example, the correlation values for Inward Closeness Centrality (ICC), Eigenvector Centrality (EC), and Betweenness Centrality (BC) were 0.50, 0.46, 0.38, respectively. In contrast, the correlation of Weighted Inward Closeness Centrality (WICC), Weighted Eigenvector Centrality (WEC), and Weighted Betweenness Centrality (WBC) were 0.31, 0.07, 0.27. This suggests that the network topology features, even without the detailed edge flow information, play a significant role in understanding spatio-temporal variation in CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicting CO\u003csub\u003e2\u003c/sub\u003e emissions with mobility network features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this section, we combined the selected network features from the preceding step with the corresponding geographic and demographical information to predict CO\u003csub\u003e2\u003c/sub\u003e emissions. The integration yields a complete dataset with a sample size of 60 (days) \u0026times; 366 (cities) =21,960. Subsequently, we employed eight distinct machine learning models and two different data split strategies to predict CO\u003csub\u003e2\u003c/sub\u003e emissions. Split \u003cem\u003eStrategy\u003c/em\u003e 1 involved randomly partitioned all the 21,960 samples, whereas split \u003cem\u003eStrategy\u003c/em\u003e 2 entailed partitioning based on days, ensuring that, samples from the same day were simultaneously allocated to either the training or test set (Extended Data Fig. 3).\u003c/p\u003e\n\u003cp\u003eAmong the models tested, the LGBM demonstrated the best performance, achieving R\u003csup\u003e2\u003c/sup\u003e value near 1.0 in predicting multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions under both data split strategies. The performance of all other models varied slightly depending on the data split strategies, with \u003cem\u003eStrategy\u003c/em\u003e 1 generally performing better (Extended Data Table 2).\u003c/p\u003e\n\u003cp\u003eFig. 2 shows the agreement between the predicted and observed CO\u003csub\u003e2\u003c/sub\u003e in mainland China during the turbulent period of Chinese Spring Festival Travel Rush and strict lockdown measures, indicating that LGBM accurately models the relationship between human mobility and CO\u003csub\u003e2\u003c/sub\u003e emissions. Despite the substantial fluctuations in both human mobility and CO\u003csub\u003e2\u003c/sub\u003e emissions throughout the study period, the predictions remained satisfactory (R\u003csup\u003e2\u003c/sup\u003e\u0026ge;0.98). For split \u003cem\u003eStrategy\u003c/em\u003e 1, LGBM outperforms all the rest models with R\u003csup\u003e2\u003c/sup\u003e=1.00, 0.99, 0.98 for predicting CO\u003csub\u003e2\u003c/sub\u003e from multi-source, domestic aviation, and ground transportation, respectively; and for split \u003cem\u003eStrategy\u003c/em\u003e 2, LGBM remains the best performed model with R\u003csup\u003e2\u003c/sup\u003e=1, 0.97 and 0.94 for the three types of CO\u003csub\u003e2\u003c/sub\u003e. XGB runs next to LGBM as the second-best model in both scenarios, with slightly decreased R\u003csup\u003e2\u003c/sup\u003e (0%~1%) and higher RMSE (7%~17%) and MAE (2%~18%). The significant superiority of LGBM and XGB indicates that, the gradient boosting frameworks for ensemble learning are powerful in predicting spatio-temporal carbon emission with high precision, such that there is a great potential of extrapolating the approach to other settings with either missing observable emission data for some places, or for forecasting future trend of emission with human activity data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeature importance and robustness analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe importance of geographic and demographic information in predicting CO\u003csub\u003e2\u003c/sub\u003e emissions was confirmed by SHapley Additive exPlanations (SHAP) values \u003csup\u003e29\u003c/sup\u003e (Methods). This analysis identified latitude (LAT), longitude (LON), and population (POP) as the three most crucial features (Fig. 3a, b, and Supplementary Fig. 3), which consistent with previous studies \u003csup\u003e30,31\u003c/sup\u003e. Notably, the substantial difference in winter temperature across different latitudes in China contributes to divergent heating demands. The northern region, characterized by its heavy industrial base, requires more heating compared to the southern region, which is predominantly developed in light industry. This climatic and industrial disparity significantly influences the regional CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n\u003cp\u003eConnectivity and population flow, as represented by network features such as WDAC, WICC, OCC and IS, were found to be crucial for predicting CO\u003csub\u003e2\u003c/sub\u003e emissions. To further investigate the contribution of network information to the prediction, an ablation experiment was executed. A null model was built using LGBM trained solely on latitude, longitude and population. The model\u0026rsquo;s predictions were repeated 200 times with independent random splits to mitigate potential biases from data splitting (Supplementary Figs. 4, 5). Notably, when network features were incorporated, there was a marked improvement in prediction accuracy compared to the null model. For multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions, the R\u003csup\u003e2\u003c/sup\u003e value increased from 0.93 to 0.98. Additionally, an increase of over 60% was observed across other evaluation settings, such as RMSE and MAE (Fig. 3c, Extended Data Table 3).\u003c/p\u003e\n\u003cp\u003eTo further evaluated the prediction performance, we conducted analysis that involved gradually extending the study period and increasing the time span of training data. Specifically, at each timestep, the model was trained using all historical data, meaning that for prediction the outcome on the \u003cem\u003en\u003c/em\u003eth day, the training set consisted of data accumulated from the preceding \u003cem\u003en\u003c/em\u003e-1 days. The results demonstrate that an increase in the quantity of historical data led to significant improvements in all three prediction performance metrics (Fig. 3d-e). Notably, there was a distinct turning point observed on the seventh day, indicating that even with just one week\u0026rsquo;s worth of historical data, the method could generate predictions with satisfactory accuracy. This resilience was evident despite substantial fluctuations in population mobility. Moreover, we conducted sensitivity analyses by considering segmentation and scaling in the training and test sets, reaffirming the robustness of our model and ensuring accurate predictions across diverse conditions (Extended Data Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneralization and extrapolation of the method to Italy, the U.S. and Mexico\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the generalizability and practicality of the proposed method, we utilized open-source mobility data from Italy \u003csup\u003e32\u003c/sup\u003e, the U.S. \u003csup\u003e33\u003c/sup\u003e, and Mexico \u003csup\u003e34\u003c/sup\u003e as a basis for predicting their respective CO\u003csub\u003e2\u003c/sub\u003e emissions (Fig. 4a-c). Surprisingly, despite the distinct socio-economic landscapes of these countries, our method consistently demonstrated commendable forecasting accuracy across all the three countries. In the case of Italy, outflows of each province were normalized on a daily basis, leading to the exclusion of weighted network features in our predictive model. Despite this constraint, we achieved an impressive R\u003csup\u003e2\u003c/sup\u003e value of 0.91. Similarly, our method demonstrated success in predicting CO\u003csub\u003e2\u003c/sub\u003e emissions for the U.S. and Mexico, yielding R\u003csup\u003e2\u003c/sup\u003e values of 0.96 and 0.99, respectively (Fig. 4d-f, and in Supplementary Fig. 6). These results demonstrate the generalizability and efficacy of the computational framework across diverse countries, highlighting its considerable potential for monitoring and forecasting carbon emission in LMICs where reliable emission measurements are often deficient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDelineating CO\u003csub\u003e2\u003c/sub\u003e emissions in city clusters through human mobility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the notable carbon footprint of city areas, it is imperative to establish specialized carbon mitigation units, tailored to city clusters, for effective monitoring and reduction of carbon emissions. The previous finding of close association between CO\u003csub\u003e2\u003c/sub\u003e emissions and human mobility (Figs. 1 and 2), suggests the latter as a promising objective means of delineating city clusters. Therefore, we utilized Louvain algorithm \u003csup\u003e35\u003c/sup\u003e (Methods) to detect communities in mobility networks during the period unaffected by \u003cem\u003eChunyun\u003c/em\u003e and COVID-19 lockdowns (Supplementary Table 2). The study period coincided with the run-up to the annual \u003cem\u003eChunyun\u003c/em\u003e mass migration and COVID-19 epidemic, which was divided into four distinct periods: normal times (January 1-9), \u003cem\u003eChunyun\u003c/em\u003e (January 10-23), stringent travel restrictions (January 24 - February 10), and recovery times (February 11-29).\u003c/p\u003e\n\u003cp\u003eDuring the normal times, significant geographical disparities in CO\u003csub\u003e2\u003c/sub\u003e emissions across city clusters were observed. Notably, the Beijing-Tianjin-Hebei (62.75 million kgC/d), the Northwest (Ordos) (60.24 million kgC/d), and the Yangtze River Delta (49.71 million kgC/d) were identified as leading emitters, primarily due to their dense population and industrial development activities (Fig. 5a-c). In addition, our findings indicate that polycentric city clusters are associated with higher CO\u003csub\u003e2\u003c/sub\u003e emission efficiency compared to city clusters with a concentric structure centered around a single unban core. For example, the Northwest (Changji) city cluster records emissions at 15.32 million kgC/d, significantly lower than 60.24 million kgC/d observed in the Northwest (Ordos).\u003c/p\u003e\n\u003cp\u003eA detailed examination of CO\u003csub\u003e2\u003c/sub\u003e emissions across different time periods reveals a dispersion of emissions throughout China. During the \u003cem\u003eChunyun\u003c/em\u003e period, characterized by high variability in mobility patterns, emissions were more dispersed throughout China (Fig. 5d). Stringent travel restrictions imposed during the COVID-19 pandemic substantially reduced CO\u003csub\u003e2\u003c/sub\u003e emissions in all city clusters (Fig. 5e). In the subsequent recovery period, strengthened local interactions within the mobility network resulted in lower CO\u003csub\u003e2\u003c/sub\u003e emissions compared to normal levels (Fig. 5f). These investigations, which delve into the examination of CO\u003csub\u003e2\u003c/sub\u003e emissions within city clusters, have significant implications for designing of customized CO\u003csub\u003e2\u003c/sub\u003e emissions policies. Such an approach can contribute to effective governance of CO\u003csub\u003e2\u003c/sub\u003e emissions at an aggregated level.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study underscores the potential of utilizing human mobility data to make accurate CO\u003csub\u003e2\u003c/sub\u003e emissions prediction. Common transportation modes such as airplanes, high-speed trains, and long-distance buses play a crucial role in shaping intercity connections, and it follows that network features derived from human mobility networks demonstrate significant potential in predicting CO\u003csub\u003e2\u003c/sub\u003e emissions from domestic aviation and ground transportation. More notably, these same network features have shown considerable efficacy in forecasting multi-source CO\u003csub\u003e2\u003c/sub\u003e emissions across seven distinct carbon sources. Also, this study offers an inexpensive method for predicting CO\u003csub\u003e2\u003c/sub\u003e emissions, which could be particularly valuable for implementation in economically disadvantaged nations. Our approach leverages existing data, unlike carbon monitoring satellites that have restricted coverage in LMICs, making scaling across countries simple and nearly cost-free. Particular noteworthy is the resilience of our method in face of fluctuating human mobility patterns observed during the study period, ranging from the \u003cem\u003eChunyun\u003c/em\u003e\u0026mdash;the largest annual human migration in the world \u003csup\u003e36\u003c/sup\u003e\u0026mdash;to the exceptional calm of COVID-19 lockdowns. Despite these variances, the approach consistently yielded reliable predictions.\u003c/p\u003e\n\u003cp\u003eIt is imperative to develop innovative and cost-effective solutions for monitoring CO\u003csub\u003e2\u003c/sub\u003e emissions, as they play a vital role in attaining emissions targets set forth by the Paris Agreement. Our research employed community detection methods of human mobility networks to categorize cities into city clusters. This analysis revealed considerable variations in CO\u003csub\u003e2\u003c/sub\u003e across different clusters, highlighting the need to pinpoint specific characteristics of carbon-intensive cities. City clusters can serve as effective units for monitoring and coordinating efforts to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions, enabling collaborative planning to achieve emission reduction goals. It has the potential to lower the cost of CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions mitigation, which is particularly valuable for LMICs where detailed mapping of city clusters is limited. Despite the continuous growth of cities, there are steps that city planners can take into mitigate carbon emissions. These measures include promoting the adoption of cleaner sources of industrial and domestic energy, implementing energy-efficient ground transportation technologies, and improving rail transportation systems as a viable to domestic aviation. However, it is often perceived that these solutions are unpredictable and costly. Therefore, it is crucial to implement scalable policies that can effectively achieve these objectives and significantly reduce carbon emissions from city clusters.\u003c/p\u003e\n\u003cp\u003eOur research findings align with the previous studies that have established a correlation between CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemissions and various factors such as socio-economic development \u003csup\u003e37\u003c/sup\u003e, energy consumption \u003csup\u003e38,39\u003c/sup\u003e, and nighttime stable light \u003csup\u003e40\u003c/sup\u003e. Additionally, it is important to note that an accurate and efficient carbon monitoring approach plays a pivotal role in fostering international agreements and collaborations on climate change, as demonstrated by the Paris Agreement. Our proposed solutions not only deepen our understanding of the relationship between human activity\u0026nbsp;and CO\u003csub\u003e2\u003c/sub\u003e emissions but also provide practical tools and strategies for effectively managing carbon emissions in the face of ongoing socio-economic and environmental challenges. These efforts are crucial for achieving development goals in a post-climate change era.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eReporting summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information on research design is available in the Nature Research Reporting Summary linked to this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aggregated mobility matrices from China analyzed in this study are available on GitHub (https://github.com/wangmengning/MobilityForCO2Prediction). To facilitate the review process, an encrypted version of the data is accessible, requiring the password XGUTZH to decompress the data.\u003c/p\u003e\n\u003cp\u003eThe weighted daily origin-destination matrices for Italy are available at https://data.humdata.org/dataset/covid-19-mobility-italy.\u003c/p\u003e\n\u003cp\u003eThe multiscale dynamic human mobility flow dataset for the U.S. can be accessed at https://github.com/GeoDS/COVID19USFlows.\u003c/p\u003e\n\u003cp\u003eThe daily intermunicipal travel networks of Mexico are available at https://osf.io/42xqz/.\u003c/p\u003e\n\u003cp\u003eThe Global Gridded Daily CO\u003csub\u003e2\u003c/sub\u003e Emissions Dataset (GRACED) is available at https://carbonmonitor-graced.com/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the code to reproduce the results are available on GitHub (https://github.com/wangmengning/MobilityForCO2Prediction). To facilitate the review process, the same code XGUTZH can be used to download and extract the code files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe great appreciate Professor Maribel Hern\u0026aacute;ndez Rosales and Guillermo de Anda-J\u0026aacute;uregui for providing the mobility data in Mexico and for further data clarifications. This work was supported by the National Nature Science Foundation of China (72025405, 72088101, 72001211), the Innovation Team Project of Colleges in Guangdong Province (2020KCXTD040), and the Hunan Science and Technology Plan Project (2020TP1013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion \u0026amp; Ethics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that all individuals who contributed significantly to this work have been appropriately acknowledged. The accountability of authorship strictly complies with relevant national and international regulations and guidelines regarding research ethics, such as those listed by the Committee on Publication Ethics (COPE) and the International Council of Medical Journal Editors (ICMJE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.L. conceived the research. M.N.W., X.L.Z., S.Y.T., and Z.Q. performed the research. M.N.W. and Z.Q. collected and cleaned the data. M.N.W., Z.Q., B.S., M.S.C., and S.H.G. analyzed the data. M.N.W., X.L.Z. wrote the paper. X.L., M.N.W., X.L.Z., S.Y.T., D.W., M.O., M.J.L., Z.L. and X.H.C revised the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeer review information.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReprints and permissions information is available at http://www.nature.com/reprints.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStocker, T. F.\u003cem\u003e et al.\u003c/em\u003e Climate Change 2013 \u0026ndash; The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2013).\u003c/li\u003e\n\u003cli\u003eEskander, S. M. S. U. \u0026amp; Fankhauser, S. Reduction in greenhouse gas emissions from national climate legislation. \u003cem\u003eNature Climate Change\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 750-756 (2020).\u003c/li\u003e\n\u003cli\u003eJones, M. W.\u003cem\u003e et al.\u003c/em\u003e National contributions to climate change due to historical emissions of carbon dioxide, methane, and nitrous oxide since 1850. \u003cem\u003eScientific Data\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 155 (2023).\u003c/li\u003e\n\u003cli\u003eCostello, A.\u003cem\u003e et al.\u003c/em\u003e Managing the health effects of climate change: Lancet and University College London Institute for Global Health Commission. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cstrong\u003e373\u003c/strong\u003e, 1693-1733 (2009).\u003c/li\u003e\n\u003cli\u003eHsiang, S.\u003cem\u003e et al.\u003c/em\u003e Estimating economic damage from climate change in the United States. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e356\u003c/strong\u003e, 1362-1369 (2017).\u003c/li\u003e\n\u003cli\u003eChallinor, A. J.\u003cem\u003e et al.\u003c/em\u003e A meta-analysis of crop yield under climate change and adaptation. \u003cem\u003eNature Climate Change\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 287-291 (2014).\u003c/li\u003e\n\u003cli\u003ePacala, S. \u0026amp; Socolow, R. Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e305\u003c/strong\u003e, 968-972 (2004).\u003c/li\u003e\n\u003cli\u003eShirley, C. \u0026amp; Gecan, R. Emissions of Carbon Dioxide in the Transportation Sector (Congressional Budget Office, 2022).\u003c/li\u003e\n\u003cli\u003eUnited Nations Climate Change Secretariat. Draft Implementation Plan with respect to Net-Zero Pledges of non-State actors and Integrity Matters (United Nations Climate Change Secretariat, 2023).\u003c/li\u003e\n\u003cli\u003eCambaliza, M. O. L.\u003cem\u003e et al.\u003c/em\u003e Assessment of uncertainties of an aircraft-based mass balance approach for quantifying urban greenhouse gas emissions. \u003cem\u003eAtmospheric Chemistry and Physics\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 9029-9050 (2014).\u003c/li\u003e\n\u003cli\u003eShen, L.\u003cem\u003e et al.\u003c/em\u003e Factory-level measurements on CO2 emission factors of cement production in China. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 337-349 (2014).\u003c/li\u003e\n\u003cli\u003eChau, C. K., Leung, T. M. \u0026amp; Ng, W. Y. A review on Life Cycle Assessment, Life Cycle Energy Assessment and Life Cycle Carbon Emissions Assessment on buildings. \u003cem\u003eApplied Energy\u003c/em\u003e \u003cstrong\u003e143\u003c/strong\u003e, 395-413 (2015).\u003c/li\u003e\n\u003cli\u003eJiang, T., Yu, Y., Jahanger, A. \u0026amp; Balsalobre-Lorente, D. Structural emissions reduction of China\u0026apos;s power and heating industry under the goal of \u0026ldquo;double carbon\u0026rdquo;: A perspective from input-output analysis. \u003cem\u003eSustainable Production and Consumption\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 346-356 (2022).\u003c/li\u003e\n\u003cli\u003eCao, Y., Yin, K., Li, X. \u0026amp; Zhai, C. Forecasting CO2 emissions from Chinese marine fleets using multivariable trend interaction grey model. \u003cem\u003eApplied Soft Computing\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 107220 (2021).\u003c/li\u003e\n\u003cli\u003eLu, I. J., Lewis, C. \u0026amp; Lin, S. J. The forecast of motor vehicle, energy demand and CO2 emission from Taiwan\u0026apos;s road transportation sector. \u003cem\u003eEnergy Policy\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 2952-2961 (2009).\u003c/li\u003e\n\u003cli\u003eWen, L. \u0026amp; Yuan, X. Forecasting CO2 emissions in China\u0026rsquo;s commercial department, through BP neural network based on random forest and PSO. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e718\u003c/strong\u003e, 137194 (2020).\u003c/li\u003e\n\u003cli\u003eOfosu-Adarkwa, J., Xie, N. \u0026amp; Javed, S. A. Forecasting CO2 emissions of China\u0026apos;s cement industry using a hybrid Verhulst-GM(1,N) model and emissions\u0026apos; technical conversion. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e \u003cstrong\u003e130\u003c/strong\u003e, 109945 (2020).\u003c/li\u003e\n\u003cli\u003eWen, L. \u0026amp; Cao, Y. Influencing factors analysis and forecasting of residential energy-related CO2 emissions utilizing optimized support vector machine. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e250\u003c/strong\u003e, 119492 (2020).\u003c/li\u003e\n\u003cli\u003eSharma, S. S. Determinants of carbon dioxide emissions: Empirical evidence from 69 countries. \u003cem\u003eApplied Energy\u003c/em\u003e \u003cstrong\u003e88\u003c/strong\u003e, 376-382 (2011).\u003c/li\u003e\n\u003cli\u003eMagazzino, C., Mele, M. \u0026amp; Schneider, N. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions. \u003cem\u003eRenewable Energy\u003c/em\u003e \u003cstrong\u003e167\u003c/strong\u003e, 99-115 (2021).\u003c/li\u003e\n\u003cli\u003eKhan, M. K., Teng, J.-Z., Khan, M. I. \u0026amp; Khan, M. O. Impact of globalization, economic factors and energy consumption on CO2 emissions in Pakistan. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e688\u003c/strong\u003e, 424-436 (2019).\u003c/li\u003e\n\u003cli\u003eRibeiro, H. V., Rybski, D. \u0026amp; Kropp, J. P. Effects of changing population or density on urban carbon dioxide emissions. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 3204 (2019).\u003c/li\u003e\n\u003cli\u003eBassolas, A.\u003cem\u003e et al.\u003c/em\u003e Hierarchical organization of urban mobility and its connection with city livability. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 4817 (2019).\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;olak, S., Lima, A. \u0026amp; Gonz\u0026aacute;lez, M. C. Understanding congested travel in urban areas. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 10793 (2016).\u003c/li\u003e\n\u003cli\u003eTan, S.\u003cem\u003e et al.\u003c/em\u003e Mobility in China, 2020: a tale of four phases. \u003cem\u003eNational Science Review\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, nwab148 (2021).\u003c/li\u003e\n\u003cli\u003eJia, J. S.\u003cem\u003e et al.\u003c/em\u003e Population flow drives spatio-temporal distribution of COVID-19 in China. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e582\u003c/strong\u003e, 389-394 (2020).\u003c/li\u003e\n\u003cli\u003eLu, X., Bengtsson, L. \u0026amp; Holme, P. Predictability of population displacement after the 2010 Haiti earthquake. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 11576-11581 (2012).\u003c/li\u003e\n\u003cli\u003ePappalardo, L., Pedreschi, D., Smoreda, Z. \u0026amp; Giannotti, F. \u003cem\u003eUsing big data to study the link between human mobility and socio-economic development\u003c/em\u003e. In \u003cem\u003eProceedings of the 2015 IEEE International Conference on Big Data (Big Data) \u003c/em\u003e871-878 (IEEE, 2015).\u003c/li\u003e\n\u003cli\u003eLundberg, S. M.\u003cem\u003e et al.\u003c/em\u003e From local explanations to global understanding with explainable AI for trees. \u003cem\u003eNature Machine Intelligence \u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e, 56-67 (2020).\u003c/li\u003e\n\u003cli\u003eDietz, T. \u0026amp; Rosa, E. A. Effects of population and affluence on CO2 emissions. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, 175-179 (1997).\u003c/li\u003e\n\u003cli\u003eZheng, J.\u003cem\u003e et al.\u003c/em\u003e Regional development and carbon emissions in China. \u003cem\u003eEnergy Economics\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 25-36 (2019).\u003c/li\u003e\n\u003cli\u003ePepe, E.\u003cem\u003e et al.\u003c/em\u003e COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. \u003cem\u003eScientific Data\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 230 (2020).\u003c/li\u003e\n\u003cli\u003eKang, Y.\u003cem\u003e et al.\u003c/em\u003e Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic. \u003cem\u003eScientific Data\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 390 (2020).\u003c/li\u003e\n\u003cli\u003eFontanelli, O.\u003cem\u003e et al.\u003c/em\u003e Intermunicipal travel networks of Mexico during the COVID-19 pandemic. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 8566 (2023).\u003c/li\u003e\n\u003cli\u003eBlondel, V. D., Guillaume, J.-L., Lambiotte, R. \u0026amp; Lefebvre, E. Fast unfolding of communities in large networks. \u003cem\u003eJournal of Statistical Mechanics: Theory and Experiment\u003c/em\u003e \u003cstrong\u003e2008\u003c/strong\u003e, P10008 (2008).\u003c/li\u003e\n\u003cli\u003eCripps, K. \u0026amp; Wang, S. World\u0026rsquo;s largest annual human migration now underway in China (CNN Travel, 2019).\u003c/li\u003e\n\u003cli\u003eSoytas, U., Sari, R. \u0026amp; Ewing, B. T. Energy consumption, income, and carbon emissions in the United States. \u003cem\u003eEcological Economics\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 482-489 (2007).\u003c/li\u003e\n\u003cli\u003eZhang, L., Pang, J., Chen, X. \u0026amp; Lu, Z. Carbon emissions, energy consumption and economic growth: Evidence from the agricultural sector of China\u0026apos;s main grain-producing areas. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e665\u003c/strong\u003e, 1017-1025 (2019).\u003c/li\u003e\n\u003cli\u003eCanadell, J. G.\u003cem\u003e et al.\u003c/em\u003e Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 18866-18870 (2007).\u003c/li\u003e\n\u003cli\u003eYang, D., Luan, W., Qiao, L. \u0026amp; Pratama, M. Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery. \u003cem\u003eApplied Energy\u003c/em\u003e \u003cstrong\u003e268\u003c/strong\u003e, 114696 (2020).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Data and Methods","content":"\u003cp\u003e\u003cstrong\u003eMobility data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChina\u003c/em\u003e. The dataset was provided by one of China\u0026rsquo;s largest national mobile operators, China Unicom, which had 318 million active users as of 2019 \u003csup\u003e41\u003c/sup\u003e. The mobility data used in this study was derived from Cellular Signaling Data (CSD), and was aggregated into city-level mobility matrices on a daily basis. To exclude population flow resulting from brief stays in a city, users who spent less than 30 minutes in a city were filtered out. The dataset encompasses the period from January 1, 2020, to February 29, 2020, a critical period during the COVID-19 pandemic outbreak.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eItaly\u003c/em\u003e. The dataset captures aggregated movements between different Italian provinces, utilizing location data derived from a large-scale dataset of anonymously shared positions of about 170,000 de-identified smartphone users and a total of about 200 million data points at the sub-national scale \u003csup\u003e32\u003c/sup\u003e. The mobility data is recorded in the form of OD (origin-destination) matrix, which was normalized by row, with each entry (\u003cem\u003ei\u003c/em\u003e, \u003cem\u003ej\u003c/em\u003e) in the matrix representing the proportion of user movements to province \u003cem\u003ej\u003c/em\u003e out of those from province \u003cem\u003ei\u003c/em\u003e. The dataset spans from January 18, 2020, to June 26, 2020.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe U.S.\u003c/em\u003e The dataset was sourced from anonymous mobile phone user visits to various locations provided by SafeGraph \u003csup\u003e33\u003c/sup\u003e. The mobility patterns were aggregated at two geographical scales: county and state. The reliability of this dataset was confirmed through a high correlation with openly available data sources. The dataset covers the period from January 1, 2020, to February 29, 2020.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMexico\u003c/em\u003e. The dataset is a collection of human mobility networks which depict travel patterns between municipalities in Mexico \u003csup\u003e34\u003c/sup\u003e. These networks were constructed based on geolocation data obtained from mobile devices. In the network, each municipality was represented as a node, and the weight of an edge (\u003cem\u003ei\u003c/em\u003e, \u003cem\u003ej\u003c/em\u003e) corresponded to the total number of observed trips from municipality\u003cem\u003e\u0026nbsp;i\u003c/em\u003e to municipality \u003cem\u003ej\u003c/em\u003e, and was normalized by the varying number of mobile devices recorded on a given day. The dataset covers the period from January 1, 2020, to December 31, 2020.\u003c/p\u003e\n\u003cp\u003eDetails of the mobility datasets can be found in Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;emissions data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e emissions data comes from the Global Gridded Daily CO\u003csub\u003e2\u003c/sub\u003e Emissions Dataset (GRACED) \u003csup\u003e42\u003c/sup\u003e. GRACED offers a daily spatial resolution of 0.1\u0026deg;\u0026times;0.1\u0026deg;, encompassing CO\u003csub\u003e2\u003c/sub\u003e emissions from seven sectors: power, industry, residential consumption, ground transportation, domestic aviation, international aviation, and international shipping. GRACED is derived by Carbon Monitor national-level daily emissions, which are also allocated to finer-grid cells using spatial patterns from the Global Carbon Grid (GID) and the Emission Database for Global Atmospheric Research (EDGAR), as well as sub-national proxy based on TROPOMI NO2 retrievals. Rasterizing the global map at a resolution of 0.1\u0026deg;\u0026times;0.1\u0026deg; results in matrix of 1800\u0026times;3600 grids. GRACED is recorded in the form of an 1800\u0026times;3600 matrix, with each element representing CO\u003csub\u003e2\u003c/sub\u003e emissions that correspond to a grid in the rasterized map.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" width=\"693\" height=\"311\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComputational framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the large-scale, third-party, and non-intrusive mobility data, our study introduces a comprehensive computational framework for predicting CO\u003csub\u003e2\u003c/sub\u003e emissions. This framework initiates with the acquisition of mobility data and the subsequent construction of mobility networks. These networks facilitate the extraction of predictor features. Next, network features (\u003cem\u003eX\u003c/em\u003e) are integrated with the additional data, such as population and geographical coordinates (\u003cem\u003eX\u0026rsquo;\u003c/em\u003e), to fine-tune a machine learning model on the training data. The fine-tuned model is then capable of being applied to predict the CO\u003csub\u003e2\u003c/sub\u003e emissions (\u003cem\u003eY\u003c/em\u003e) in scenarios where specific time-location pair of CO\u003csub\u003e2\u003c/sub\u003e emissions data are unavailable.\u003c/p\u003e\n\u003cp\u003eTo perform the prediction, we divided the dataset into training and testing sets using two different strategies (\u003cem\u003eStrategy\u003c/em\u003e 1 and \u003cem\u003eStrategy\u003c/em\u003e 2, Extended Data Fig. 2). We then applied eight supervised learning algorithms (Multiple Linear Regression (MLR), Polynomial Regression (PR), Least Absolute Shrinkage and Selection Operator (LASSO) \u003csup\u003e43\u003c/sup\u003e, Ridge Regression (RR) \u003csup\u003e44\u003c/sup\u003e, K-Nearest Neighbors Regression (KNNR) \u003csup\u003e45\u003c/sup\u003e, Support Vector Regression (SVR) \u003csup\u003e46\u003c/sup\u003e, Artificial Neural Network (ANN), Random Forest (RF) \u003csup\u003e47\u003c/sup\u003e, eXtreme Gradient Boosting (XGB) \u003csup\u003e48\u003c/sup\u003e, Light Gradient Boosting Machine (LGBM) \u003csup\u003e49\u003c/sup\u003e ) to make a crosswise comparison of their ability to predict CO\u003csub\u003e2\u003c/sub\u003e emissions. In conducting these experiments, stratified 10-fold cross-validation was employed on the training set using the above eight prediction models.\u003c/p\u003e\n\u003cp\u003eTo optimize the training process, we employed z-score normalization during data preprocessing. This step involved normalizing the labels of the training set prior to model training and then de-normalizing the prediction results for final output. The effectiveness of the out-of-sample forecast capability was then evaluated using three key metrics: coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), root mean square error (RMSE) and mean absolute error (MAE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSHapley Additive exPlanations (SHAP) values\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSHAP is an additive method to overhaul each feature contribution to the prediction results in the learning model \u003csup\u003e50\u003c/sup\u003e . SHAP actually attributes the output value to the shapely value of each feature \u003csup\u003e51\u003c/sup\u003e. The output value is given as\u003c/p\u003e\n\u003cp\u003e\u003cimg 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tQmYgAmYgAmYgAmYwPYIOMjd3pjZYhMwARMwARMwARMwgQECDnIHALnaBEzABEzABEzABExgewQc5G5vzGyxCZiACZiACZiACZjAAAEHuQOAXG0CJmACp07g+fPnuytXruzIq0T5rVu3dn/99VdV7TITMAETOEsCDnLPcljtlAmYwCURIHh98ODB/pP9/vDhw+7evXsOcDMY35uACZw9AQe5Zz/EdtAETOASCFRB7pcvX3Y3b97sPOG9BC720QRM4HIJOMi93LG35yZwdgQU1BHwXVLC73//+9/fndhyuvvkyZPd3bt3d5zmOpmACZjApRFwkHtpI25/TeCMCbx+/br33dQlXX/16tXuxo0b5SsCS+khOEXH48ePd1+/fu0U+/bt292bN2++C3L//PPPH8o6BbjCBEzABM6QwOpBLhvvo0ePdnfu3DlDfPNd+vbt2+6nn37aPXv2bL6w/0r4+PHj7unTp3u5iwk9giD8YO7A55TS+/fv94EU+Tkm5iQBHGt2zLo9hfHiD6yuXr26+sklQSd62N+UODmlnD8A48P1mD/0QtbDhw93165d23Eyq0RwK31dJ7IEtPS5ffv2PifYJ/CFR9cfo0n+UM7exBpkXsxJnz9/3r18+XJ3/fr17/a7NfbAOXau3TdyWGMPOcdnbtyTpj4r4fLzzz/v1yZzkD3O6XsC57gWVw9yWcRMqDEPy++xn/fdUpMqLliCjfv37+8X85bpnYofkS08LyHI5UFMoDZm3Z7CeFXvpS69BnRaTB4TuvUNBgS3//rXv5pPeQlEObGFeQ5y0YE8ZEt+1EtwS0CrIPfdu3c7gl768AdnXYFxlNF3vWSQS6CBjzFQWWoPrHxgHq8RSFa6WsuwCf/hsIRt2cdzfOYyR6q508qcvsQhyIEX+9pShyeZf6tNp9hu6lrMz8hT8m31IBdnmVBjHpanBGgLtjDB4kMDm7WJbsH+PhuP7UfFts/ec6obG+Ti+7HHi0AzB59LjwkBKWyiHq7zCTLBJWWx3ZAt2N8X5FZ1OsVVIMypLwn9p/atCvoBMe9XQ1ym1vOboCUCyan6u/qJwxK2VT6e6zM3/4DUxTeXr8mj4p/1n/O9fmg4VR8d5J7qyDTaxelZ9brDsYONRvMHmx3Tjy62g0afSYOtBbkEeb/99lvTKwKfPn3a/0EWPubPUFBKPX3iawDxFFfDr6CTutbUFeRySkuAG09yJR9bVP7rr7/uT3SRI7/G6G+1c2o7BXeHCHL5ARUGSwSSU/3t6icOc23r8nHNoK7Lp0OUM55T5s5aPLr4H4LFKejg5JdXQOB7qqk5yNWGSY5DWqSxHCdjuSajJhhA+KmHPvzqgJ8AYor1tOFX7pSRaKv3TClDJsGdNgkCEsroRzltTz3hB4sEu8UKm+WffKFefkafYIKvtNNHcsgpI3FNOz782iYmdGlMaB+Zx3bxWvJozwSHPUk6oy1xPuAHifZxLPVqBXMi2yeZ+47//Yc2ercq24w/eu8P3fo1Fe3ynMAO6c6+dLGlPMqPdqFPdsEartijRD1lcIjc8VsM1Va55j1t6C/2yGfukLCH+8pH6ofsog32aEzElFxjtle0233HE5tkg+qr8UI/srBzblKAibz4GRPIIWPONw7ohFY6FYDqPvpIWXX6GtvE6672WWfss+Z1XKvSM2YeM3+1xpijzCfGjXlCYt5VeyB1eX3m9dS3D1AX5wfX2C2dfXseepnb9CFnTVdJvkgPbXStXDp1r/WkNUEe9yhxifq05pER99s+H9HDJ+8zjEdM+Co/GJ+4R2Kb9isxgQcyqxT96NobxDXrkjxkxD0084ABZdFuMZWMmNNW7JXH9lFOZVO0h/7x+VjxF0vZiS0a61imfR2d8KQf17QlxXHLeqN/8bprnmQG3EebxAOd1VqUfdiBjbSXnRor6vhIluYLZdUaol3fXIp+zb1uDnIZFE1QKaUMp3EUEEoMfpwMOEQbHnK0Uz8WkBLlakMZEAWUe6Bps2SQNPnIBVTg0QNc2p1ygoMWSrQVP7XZ0AYu8q3yp/IVeZTDAhmaqIyh0hBztYs5Y8ZH44htjBP3JOxEL2VK+BMDHNowJ2iHnzBgcdGHMuxVkh+6p4424iV9kZf6YCfXtNHcYa4owYJy+cI9/sQUdVFOf8lHrhI+YL/KyLlHXmQjHdhGH40/dlQJfbTFDnKxkT/4LZ2yK/rYYhd6kYetmivSqU2LNoxR1Ub6aSMboi/UZ46xvvW6Oqml7I8//mg6vUUPASm+kk9NCjh14qv7eLIr2ZTl1xhUV+VdQa5ObatAupKzVBljp7kmmZS1zGPmPe2YS1pjzJ84F5hvWgPMHSXmMOtH+4baIIs0tA/QRvOOXAk7sEFyqUNPnOfco08yuK8SssRGOijT3qa1Sl/KYaEy2RbXL9ewURv64W/mhz3II0mO9O8L//uKoPykLTLpJ360gzE2qS9M4thQrnFmbKjX+pce5UN7A/XIli7tE+KMHD0D1EY8KFdCBsxVpnmhe7XLOWMSx5j6If9hlnlwj11K2Br9ktzYrypDt+YOLPADtuSMl8aOvuhg7LL9soG8dZ7EuYx+rQNk4K94xrVIO/lMG2zTGNGvYoseZJFk//7mv//Qhzaax7Fu6evmIBfFTCQGjwFS0mSNxgIEGEo4BJiYMhhgxwVIW01y6ZOuKJt2cdJLB/rigKp8bN73a83qoTZWPu3zgoBNXLT4GydV1pH7Uy9WcVxUpv4tzNWWXJtCLNOiiItF84Tx4xq9OcmWaB8+4kucB2qn/tiQ5yBjDTMlydEio1xlkSPzI9qt+SY55BXbShYPA/rHhGz6Rx0tayHK4FoMou2VDSqLvFvs0njFdcW4YHvkCq+qDetPSbbqfu18TICLLQSmc9Yt+wHvuf7+++9/u3aIIBdl2ovi3vC3ESteVGPaMo9Zx8y/mLRfxDlKfV5nzKm4D9CG+ae5NmYfiOuGtZjlat3rOYMtcZ738aZdXuNah3H/4TrKqdqoTPa27Le5j1i3jE/LcxM5eQylI+ZDewPcaaOk/SXOA+ojI41x3D9hnffZPHekI+b4wSemIf+lX/OCvl3PGo2Z5Fc25TKtqzjX6N8yR6WHvGWe0A624sd1ZB/lZTvhFscFe6O/FVtkRL9i/6jrENejglwmJhMxbhKavAJGG21EcqCCQFkMfLkHTPURIE0KyVVe9VFZBK32rbkeXvoqIE5Tuk5aWmVW7bBV/KiPTOE5lHJ/2lesVCaZLcyjbvUX25jHOUEf7qnPc0HyJEv3yplf2KXU1Y565oV8iH2qjV9lyMuJOtmLzTFxn/tIFjlJ95qn6g9n+kfbuI73tOU+65UMcjGQPsqksyqTvWozZBdjVD3Iou2SRVn+xL6yNdq/1jUB7tgTWQLcbH++1wlttpty3n0l2IxJ+0QVPI/dL1raE2Af8g/KqjFtmcf5WQEzzSPNUXFkDGJZvle7Kh+zD2it5THnXutE60H3lc5Yhsy4BvCRe8qVkKl9lzJxIFdSmYJjca9s1X6rPlEO8rrGJz5zK7kq03OzkiN7lcsG9Y155EJ7GPB8o5x2GnPJyH5Ih/LYJ5blwFd1yis/op35Wv6r/5g5Rp8uO+UvbTS+0qEcW7M9uq/mpOSoTcw1TyRbz7mu5zLt6B/tHIpHKrZj15DsWyP//oneoAFIbF5MVk1YJpgmM4OQB6KCoIGUSu4jWJXHXIMZy7hmUIYWR+7Tcs8DR3/QofYtDyG1bc3zpKIf/sAE1kNcqv4VK5WJVQvz6AP96dOSmBvYpbmS+8iWXI78qKNqh/3MNxaSOMU+lOU5oTLkKbGR0Y+Nnzlb6arYShY5SfdsBjnRP9rGdbynPfe060qyS/poJ51VmXxUmyG7KpvQEW2XrC4bVS5bdb9WzjcK5GCzRReBaBWMtvSlTXWaeoh3ctHND9l8j+6LFy+aX89o9auvXTWm1ZzJ87hv7WiOSm9um+/VLubMybH7ADZm3VGmrtkPkM1HQafqck5b7NVaRD59KGOP4ZODDa0n9UGmymQfOfb2JfWJcmjfOj65X9ZVycltZEMuz/cKlogXYBLHWDKG7Il9JJ+yIU6VH/Qb0kf92DmGXV12amxpwzXtcsLW2C7X5/uWeaI+Q89l2lW2wwG7qnikYoucMWtI9q2R/0h4QIuO8BUUMFljGQ7nVEGgLA4w9/GnTMlgUDQRuyYFcqqf5IY2J+noyhXQ6mGqzWzOQ7LSVU0qtcMHJlbln9pU/StWKhPPFubSQa7+jElOmTUBKLywHT05SVYuZw7E9rkdtuMvspVoH/uojfykncqQR8IHbIsPn6yLdhVbyZJ83VdjRP/4U3O2FR2U0a4ryS7po510VmXyUW2G7EI/LHLCJnGVrDzO9IllsjXLWvKe74Tl9HRK0mksAePUhG6+01Y26H3Z/AOxytlHWpP2nOqEmroxslp1DrWrxrRlHue5jx7NI81R6aZtLKv60lbrXnJ0T122SW3IlWgz9JxRW3L5Hud4rNe19hJ0ySaVsf7ir7zpU9mmMnGQ7r79Vn3IY8osqKMMrkpcV3tD9LWSo/7KZUPspzqV4Qs8Ioc45pJR/UAunsiMfaSDMuzsS5UfQ/7Lpqg/y1Eb8pi67NTY0lbjG/txjY4pc7RvnkjH0HOZdpXt6s94Mo5x3mQmaqtcfmouqPxQ+T8zfoRGBoBPdJR7fuKJZRJZQaAMmEpMbu7jhKIOeRo8wVIf5QxcXkDUxQBGbcfkOjnBLj482LJ9Y+R1tUV2nvyxbZffapP7U171UZkWZAtz6SDXDzOZK5M3TmDqtZlRjn15XsiWKF8/3UfGuV2+p3+eX9XGozL6k3QvFpRVsiu2VV/mHx/NVeRxTf/oT7aVdnktUBaT7Iq2VjaoTD4io8Uuxgs7o3z6UoZtSshinUcfGbOoT7aqz9I5AS6fqUmnrl2vJLTI1esJMeBEXv4DM7Ubo6sryJWsqLPF1iXaVGPaMo95HuQ1Uc1RbMzrTGsizknmnfaRFpukK8po2fPifMa2ytfMFbvwIe6NlOF/XEPqV9mmMulv2W/VJ/rYZbOYyoaW52aL78gb2hsqOXHMGVtk5PkCgxj4xj7yg7KKserJ2bdymyH/p84x9GGT5ir3ehZobCmr5FPeMkdpp9QyT2jb8lymXWYcbaY+212Nbe5TtZH9a+f/RJkjNPHQBoQCGbpqYHjoxcTgVpsdkw4Z1JPUjjKAAEm55DEpqY+TnjoNMnUMJH3RmRe+5IzJef8tBnBj+ra0le34piS/YcKHOsq6UtxIZatYRQbaiNWmhXnWKbnwhTO8o23c84lJYx3Hjb4ar+gnbWOSPs01zTMFjuTYQj/qmH9qgw6lHGyLu2yFE35gE23FqGIr+eRKWhPIkz9cR3/EO2/k4kN9lZAju1Rf2SAf5RNtW+yCGTbx0XyRLGxTmXTSjrnEB/bR7jxe2KB5F+2SH2NyAr0c4PJbFl5dGJMUkGoOjelLW/ozHvE3OvHUlmvs4mQ3BqVqQzAc/3BN+qnnf0njK8fwNSYF51FerJ9yzbgzfnzyvh3l5TFtncdxDkl+nAta01qL6FFizsFYc421zFxUH81FjSF53gckl77YjEzZjmzts8qlG52SK0ZxL1G7mNMOmXFPkH7Jiu1lf5QrXjBSEnt8oy1rKO630lH5iB9xbeZ9Rn2xG7nIQI/We2RF274kfzReeW/AD+qQg1y1l15kox9b5Csy8FU+yN7oP3X0wbeupH7IlSzaqrzLf9mo8WudY8hGF/4ypvSTb9guvhpb9MSEjfTHLtrTV3lsF68lS+zyPOGeT0yaD1G/mCBPSbqxiw91lClxr7mGvyTuxS2vIe6p54O+tdOkIFeORuOqMuoZqPjR5pXLaIvzGiwAMLhKQI19Yh1tkKtBY6AFW/2n5DzMqgfOFFlVn8xCE4fJqEmOzzCBb1diMtGO/rSrWFVlyOtjXulDPpsP44NObJVtWsiUayHHMso1biqnP+VZFrorm6N/1GO/7FcEqJoAACAASURBVImbiWTSRrpiGfLpRxmssbd6yGS2WZb8QR79NW7wQb7YUC/9yvP4Uy5utCdVDHJZn4/IGLKLNmw2kqv1gw+MT9yI2BDlI+1jnfrLP7HR+CBvalKQJ9nK8+lpq3y9Wys5MR86eSW4pX1uR5DKH6lSh1353VkFudTn785V4BztiAGt+uZXIlr9rdrpYcN4aqxyuzymXXM22s215nGce8jS/EEf+rM82ijFvuztca4N7QOSof2FOag0tOehS/ub1rH69uXVPh39UV98j7xoU5XRHj+1fugT91vJyz5G2RqLqoz+8O96bnb1kd6ca2zph09xvLjWvqF9Eb2UxXZwEPvIM8+TLr+yTZkr/ShT6vN/zhxDrvzQ3NNcYv7BJ/KNNmHb0ByV/cr75klkgF2kWCYmmbHmLvNLY0fbOC7IYvzwLY5l3xrCN7XPfsufJfNJQe6SBpyyLP2KUJORP/rIJyynbP8p26ZFdso22rZlCWjTXFbq4aUR5E4NrmWt/mcy3bfkBL05OG7pN9SGh1s8zRlq73oTMAETmEuAfcdB7lyKM/pzIqPjdsS8e/du/5fNBLzx15QzVFx0Vwe5lzX8/LSvE42te64ffvNJbqtf9K9eV+jrr5PceLrb135MXXU6OKa/25qACZjAWALsO5zqrp18klsQ5uHVdWJCgLvGg6Yw46yL9OuaQ0zyswa5Aef4YfHcTgp1mht/EB4aCgJVvtuXX8OPSXq/9+bNm6O/F7hPD2uPcfEa7KPkOhMwgSUJsN9wyBVfU1lSfpblIDcTCX9Ywq8UeTAp8XD65Zdf/MqCgEzM9fqH8kP8ymKiqe5mAp0E2A8IPNf8oZdTX77VRf8hTacxrjABEzABE/iBgIPcH5D8/wJOaHi4KBDjHTzeyeVUxckETMAETMAETMAETOC0CTjIPe3xsXUmYAImYAImYAImYAITCDjInQDNXUzABEzABEzABEzABE6bgIPc0x4fW2cCJmACJmACJmACJjCBgIPcCdDcxQRMwARMwARMwARM4LQJOMg97fGxdSZgAiZgAiZgAiZgAhMIOMidAM1dTMAETMAETMAETMAETpuAg9zTHh9bZwImYAImYAImYAImMIGAg9wJ0NzFBEzABEzABEzABEzgtAk4yD3t8bF1JmACJmACJmACJmACEwg4yJ0AzV1MwARMwARMwARMwAROm4CD3NMeH1tnAiZgAiZgAiZgAiYwgYCD3AnQ3MUETMAETMAETMAETOC0CTjIPe3xsXUmYAImYAImYAImYAITCDjInQDNXUzABEzABEzABEzABE6bgIPc0x4fW2cCJmACJmACJmACJjCBgIPcCdDcxQRMwARMwARMwARM4LQJOMg97fGxdSZgAiZgAiZgAiZgAhMIOMidAM1dTMAETMAETMAETMAETpuAg9zTHh9bZwImYAImYAImYAImMIGAg9wJ0NzFBEzABEzABEzABEzgtAk4yD3t8bF1JmACJmACJmACJmACEwg4yJ0AzV1MwARMwARMwARMwAROm4CD3NMeH1tnAiZgAiZgAiZgAiYwgYCD3AnQ3MUETMAETMAETMAETOC0CTjIPe3xsXUmYAImYAImYAImYAITCDjInQDNXUzABEzABEzABEzABE6bgIPc0x4fW2cCJmACJmACJmACJjCBgIPcCdDcxQRMwARMwARMwARM4LQJOMg97fGxdSZgAiZgAiZgAiZgAhMIOMidAM1dTMAETMAETMAETMAETpuAg9zTHh9bZwImcGQCDx482D1//vzIVli9CZiACZjAWAJnE+TeuXNnx2fN9ObNm92jR49W17OmD2Nkf/v2bffq1avdzz//vHv27NmYrkdt+/Hjx939+/d3V65c2f3000+7p0+fDtqDr7Tdkp+VU8xRxgvfr1+/vh+/qp3L2gh8+fJld/Pmzd2HDx/aOjS2utT9irXJemStHSJ9/vx5v6ZZC+/fvz+EylV0bM0P9lOelYwzexH7MWWHSlOeAYey7Zz0bCEmmhXksvFfvXp1P4mvXbu244HQlV6/fr1vx4Qfatslo698rYcGQZ4SmySb5drBtPQdO2djffny5X7cthL8YTMbK5scmyoPVOYc933plILcOOf6bM51bDjMT3yBA/N0qWACeVsOEjKr1nv2rVu3bu3++uuv1i5N7c5xv4qBhQIbyuIPmbFNE6iZjZi37F3Ys+X5uzU/+EGbZweJnH1I9zOHdLA7rKY8AwYF/7fB1P25Vf6pt4v+byEmmhXkajD4dR7BbtdpB8EvgS0bTQSk/qeaY2sO7tZ6OJ0qAwImxi1zOFV79UA7Vfta7CJQnZLWnJucymw5SJjCkz5belXhmPuVAgvmCXsGifmi3ypE/odeo9ix9SBXPLfgx7F5rzm/qjUW5/a5X1f+r/ncWYLn7CCXAPbu3bv7IJdTjyo9fvx4v8mscSJS6VuijBMHfhrMwd2pD+gSvmcZDnIzkfXuCRLgPSWtNTfZ2LbwcJ3CrOrz9evXHXvWL7/8st/XyB8+fLj4aW6le2rZsferrsCCgDf/0NbVdqrvQ/2OHXQN2ddavxU/jm3nWvOra421jt8x2umAkR/W56Yu/9d67sy1V/2nPU3Ve7fbn97+9ttv+4dB9ccZBL7/+7//u/+V3xKgg+q/L7veC9GvqnlAMxCtvy7RqQT99FGwqwFl81ZAwiZOn5hiPTLGvpNEYIEu9DK5uJYfOimRbeRKahfLkIV+6ipZ2K5++EKbmJCFHcgh8OcetrJDbfXrcuqRQ3ul6I/kYFNrQhc6pZ8TIvQpaWNFd/yovi9HdrSPtpQxX/AD2dG3+OvXOPfgppMrsUaWNl3s0jyK9qoszlf50Ge36qJ89UO/Uhxz+EX7aYMPspv+ca5SJ5nKNVe4l+2VP8wrjRk86Yd+2pIo0xrKeqnHbvhTR46NMSGPcuQslfht1I0bN/b7ml5VIOjlB/Sl9q84Z6Ldcfzx7ZD71dAciXZW15qDyMlJc0Tlass918wJPnCJiTqNf5636IlzizmreZLlaG5q3kkH7SSfXPuV7EMeH+6VVEY+lKRXfRhTpVymtYCf1EV71Efy5IdkkCuhQ+UqI5d81cU1HtsNXWtPRA42Rjb0lfyYy94h2YypxpH+rPdqPnXJEZ+om2ulIQZ9+quYILLuG1v0x+dL9fyDo8Z+rN/yb8288l9jj+98Il/mBn1igq+Y5fUc2y19/c8MmCj5999/329OvI6Qg1weGPfu3du9e/eu96R3oup9N8AyuYEaJxqLUeAFX4PSqo8Fkvsgk0mIfORq8HlYK1GuNpRhH4Ma7VPbKqc/GzD6WfTIRga2UBYfftoUopxYhiwt/rhxMuGQhU3IlS+0oX9MsR2yJD8GSyxcfIYHshS80D77g/30beWBLchGJrKifD2YZK8Y6b4lx2bx1nhTJlno5Tr6Dj/soIyxlX3cqx8slcRb8imvytRX/cbk8MxM0YEd2EWCPeMpO/Az3tOO+zi2KpMM5FS25zLuNVfQB2M4kcOOa81lZOc1wj1tSarf3/z3H3ylDbKWSDHARV58VYHrJf6WQHOGMYljBQfuqeejOTfGrziO6odMcUYu4w0z5CsNzRG168uRjVzZwH1X0hzHZ+yhLXbG9cK6RpbmnPrE+UAfzVXKFTxQhlwlZERZlPftV9RLX1wHlMvPKF96qlx+MAYxIQd/xYnx4F48tG5UT9/KD7WLsnMZMjQHJIexgt+YBAvkyHftJXEuSX7m3aIH/7Ede9HBfebWIkdjF9u2MGjRj1/IV0Iu/SLLXMY985O++Ae3+PyDHx/a4Tc+L7mvydYl8uw/MvFd80s+rLHHTLV/dpD766+/7j59+vTDSQd/qEGAy4ODE5G+d3anGh/7ATpONCZiXnxsOGNS34BGOVk3kzjrZlIjjwdKa6J93mSzTdWCrsroF/lgQ7ZbZbSNKeuM7bThMal1TT2TnX4saiXuMxfV9eV6UCBTiWs2F/TGVPke6/uus596qOjBSl+VkSvBMW/GsiPOuSyf/rlM/SR7TF6NJ/yjDcjTJsq1AtM4L6mPc6Xymb7Z9qpM/sS5QbuWNYL82C/7gZylEvtVPK3l13zxWxWWCnJlbx4rOOW1MdbfajyquZl1D80R2TyUsyaRhR2sS3yKa1b9NSdincrUhvkR1zZts3/qE+VorkaWKotrtmW/ghN7TEyMSd6TY3113SUnji/c+CgpKIo2V36IgfqR57KWtRb7V9esQ/jHvZB2Gu+4f1R2VjJzGWOCrUp6Zuq+Nc/+06+FQYv+PAeRnddTVxl947ykHdzyHNPYRxa0HZOIudDHZ8nXRLv8z8+/zIR5Euc7vsTn0Bjfxrb9PpoZ2ZsHAUEuiYdAhMmprk52c91INU3NM1QWGpOWSRUflE3C/tuoa0DRFRP3cZC51wTLeR7oKCdfV/opi/qrBV2V5X7oQk6UpTLaxlTZgR+Uw1mbGvf5ExdwJSfq6brGxihH7bQJol+p8l11Q3m2T35F+SpDj1LFUcFjbJfl0z+XzbG/sgP5XZ+8LhhTZNCeXEk+Rw6V7VVZlz/SU9mmNcLGyLjrXvaskecfxLln3yLlAHgJ/XmsYHus/aoaA5XlOdLiO75ofPEpj181J1QWA1Z0cc/DnnmATbRTUh/dK0dn3/zVfJaPMY/7jNpF+5E7lkklJwa0spsc2exr+IBd9FWSnFhWMchlGovop66jb9JT5ZKZfVdARr1SZafqWnL689yWjS19YhvZGsvGMOjTj03RV3Qgm09MVVnVV7bK15jngDjKb7nmgJHDRcVhLX2G2lQ+VL5SFmOi6Fe+znNqyIax9d9HMyN7A5HXFUiA1K/zKOcUl4fDGg+IyswKNPC0WMjzBlrJiWVjBpS2StiSF4LqxuSVfsqQr6RFonvyqiz3o13FjLLoC+0qO+JGputoQ3Vdyana5TJsYtPPSX6iX0lluh+TZ/vkV5SvMvQoVRypy/LyfdVmjv2VHeiM9svmmFPPw50HL9dZjnzOcub4g47IMNoTr3kIYxuffIoU2829zie13BPokvSw0P1cXfTPjCk71n7VMkem+Mx84UGH/Dh21RxXWZxjBLese4I+2OT5pj7Ztsw2z1/d537VPbIU+NKvKzit+sYyzWHKkJPnPs8mBbf4reCRtkqyO5ZVDHJZ61qTniqXzHhiSzvZFP1RWbSzkpnLGGNsZc6w7qUztxu6r/q1MGjRn+cgtiCbT0xVWdUXW3PfKGfONTHZ0r9Br3yofKWMtkpcj50P6js3/8eKCZIIcHkAkHQSwuJkI+CUl7TGA2IvOP1TgVYTFiYLJ/5kobq+fOqAYkuli41szEBX+ilDvlK1oKuy3I/+FTPKaBtTZYc2YdhqU4sPMvWPZZUctevLZVP+IUV+xo1XZX3yuuqyffIrjpnK0KNUccTWLC/f0z+XzbG/sgP5PDxz0rjIn3iik+WoTeRQ2V6VdfmDjjFrRHJkd/Znzn3+C2Tub9++/fcetsZvojLjaP+h96uhORJt67pmfPL6pK3WAf4qaSx1T64yzTHuCXDj2m5dK8yrqC/PX91XcymXqS3rg4OSXB996LumP/bTHzkEVDFpPYih9JIrVWXipjbkuUyyYxuu0RXl5/p4L5nZf9kUf7WuslbZsoXxho2SdOq+Na/6DTGARYv+PAexCdl8YqrKqr6yVeMeZWTWsa7leo19q/Kh8pUy2ipx3fccUrs18n+smCCdVxVyMMtPDvHEQ8GvguEJapq6ZNAsujhxpiy8qQOKbvrGwAEnGORo05BjlX7K8FWpWiTooV1MuR91mZnKqr7oiYnNSCcclLNB8HCJ/rGRx37IjfdRXt+1eOa++BltQEble5/sWJftq+aMyqItFUc91OLDDPnYpwSrrBO5lE1J+eGODH7gzMEC5XqYVPqyP/KZPKY5/mhM+9ZIZIzebFe0Zc51DnLZs3hAkNi39G0LXPOerva8OTqzL8fcr4bmSIufjBU+VIk1Gk9AqzmnMs2xzAe5LWuF9Ua7OK+q+duyX8kXbIknsSofm0un1l7sn32rbK7KxC3uu3kPbFlr0Zbqmh82sBEWMRGIUR73OZVpLGP7rus+37r6dJVn/2k3xKBVfx4nZMMk/8Be7cVVX3HNcwKGc4Jc7WlLvqqAr5UP1VqljLZKS+wxkjU2/8eKkT3Z8PkuSSVB1cOBcv4gjVcY4ru6ar9kzgLXJqTFzuIHrBYf92wyqm/RT3staiac9GQ5TGgGVLLVjjL6o1t5i17aaPJLP2XIRWZcUAqmWCQsVBY4ftOOOvzXxh/7yUa4yW50yBdxo0w6darChgED3dNGmwjl2MAnypY/sYx+LQn7sAvZ2jjJuY8bgXzC3mhbiw7ZFx/G8onxU0If8vFPiTGijPYk2OFnbEM5ZbKZsUEu/egvv6STe2yK4yB9VS77M1+Vo4c5gk7aZH0KCsiphze2oF8y6Atj9W3xR3NRbGR7HCutDeVqAyvZJaYaC+6p54N9cxP7ll634ppAlz2O78glXzLJ9zhW+HWs/Urj2zVHWnzHfvqTa87iJ3OOcs0ZZGlOxDLWCu20nmmjsUWO1oXmMHKkkzLa8KFf3Odop75xDqoMHejmE8cj+oyd2Bb7x/rWa9kb/VZf7MBufICfuKFTemWz7unL+sA22iMXP8SXOmQhE99opzWmXPpb8mgT7ZGNzeiMSfqjnbG+utYcRAcJX7BRc0Lzouoby6KvcV+I5fJdOf1b9TNO9CPJpsiFMnjAhbb4gW7Jr+aYeFHHHEGedETfxlwPHS4iH/vkQ6vs7L+4Us61Ev4zdiqT/5qr+Im/1VqQjKXy0UGuglmM5aMHA+/e8oXpOuXgQaE2ygG/RpJ85ehggQk05VzHSd9ihzYQJgSDJfnKGSBdK9egsQFo8jIBGNTW1CVXOpRLnvQwafARXdiM/a2yutqhg4UgHejmGv9ygjk20Ab94l3Jzn2H7uGvzQT5jCdylSod2NCScl/6wVCc5U9Vhnza4zc5bbvGGz3U0UYPBu65Fk/8xDfKGb+WlO1CPmVK6NVawM64saFPdpNjB/Zk/WIvu5E95I/kYk+2if5Da0Qc6CtO8om+lOFP9FX1Y3PtX/k/gOAHdaUYCKtsSi4eypFx7P2qb460+MgYMJeYs5pr+MdegWylak5UZewd2kuYc1oXlGlf0bzX3EQf17RVUhuxRpdS136l+pgz16LcWNd6Tf+oP/aDGzr4YJfmN+3p1+eH9maxoS394v4xtNaiLX3XyMZGeKIPW2MS55jH+r5rxllymTPsU9zHPaevP32iXq4j7yEGLfphKrmaD8jVnNd4kTMXsamyK/qBHHSLa57DsW3rNSe4HC7yHd/8PwbsbzFhL+MX+cT6ruvsf+Zd+UoZiVyc0B2fQ136ligfHeQuodQyTOCcCLBRjN0szsn/Y/rCxsmDd6nEw4Fgtkr5t1dVG5cdjoACv7U1ElQTeDiZwFYIsIfx6uiLFy9+CHCjD/yAdO7JQe65j7D9W52Ag9zVEXcqIPjgJGWJpG+C6fqNEwFwV90S+i1jHIFDBbnMMZ0ej7PQrU3gdAlwCq9T1tO1cr5lDnLnM7SECybAr5r4NRO/htGvry4Yx8FcJ7AlyFky+OCktusPywiA9Z/bHMxJK+olwA+X/Lp0qR9yojICW+Tza+RLOO2Kvvv6/AlcSoDLSDrIPf/5bA9XIsBPwdU7SSups9iVCQy9qqDv/l7ZDItvIJDX3ZKvrKBe77kS5PqH14YBcRMTOFECDnJPdGBslgmYwOkQ0B9yxD9EOx3rbIkJmIAJmEBFwEFuRcVlJmACJmACJmACJmACmybgIHfTw2fjTcAETMAETMAETMAEKgIOcisqLjMBEzABEzABEzABE9g0AQe5mx4+G28CJmACJmACJmACJlARcJBbUXGZCZiACZiACZiACZjApgk4yN308Nl4EzABEzABEzABEzCBioCD3IqKy0zABEzABEzABEzABDZNwEHupofPxpuACZiACZiACZiACVQEHORWVFxmAiZgAiZgAiZgAiawaQIOcjc9fDbeBEzABEzABEzABEygIuAgt6LiMhMwARMwARMwARMwgU0TcJC76eGz8SZgAiZgAiZgAiZgAhUBB7kVFZeZgAmYgAmYgAmYgAlsmoCD3E0Pn403ARMwARMwARMwAROoCDjIrai4zARMwARMwARMwARMYNMEHORuevhsvAmYgAmYgAmYgAmYQEXAQW5FxWUmYAImYAImYAImYAKbJuAgd9PDZ+NNwARMwARMwARMwAQqAg5yKyouMwETMAETMAETMAET2DQBB7mbHj4bbwImYAImYAImYAImUBFwkFtRcZkJmIAJmIAJmIAJmMCmCTjI3fTw2XgTMAETMAETMAETMIGKgIPciorLTMAETMAETMAETMAENk3AQe6mh8/Gm4AJmIAJmIAJmIAJVAQc5FZUXGYCJmACJmACJmACJrBpAg5yNz18Nt4ETMAETMAETMAETKAi4CC3ouIyEzABEzABEzABEzCBTRNwkLvp4bPxJmACJmACJmACJmACFQEHuRUVl5mACZiACZiACZiACWyagIPcTQ+fjTcBEzCByyDw6NGj3ZUrV/YfrkmfP3/+u4w6JxMwAROIBLwrRBod19++fdu9evVqd+fOnf2no9kmitfygYfNs2fPdtevX9+9f/9+Eyy6jIyMNPY///zz3r+uPnPL0fPTTz+tqmOujbH/X3/9tXv8+PE+wLh69eruw4cPsfrg1+h/8uTJDrtOOWEfdn769OkoZs4Zt6UZj2HB/sIenBNrVSmuW5U5NwETuGwCFxHkVpvjmGEnANGJQdxUx8g4lbZrPQgU5HKacopB7pg5EBnh18uXL/fBHEH8Uinbs6Ug98uXL7ubN2/ufv31193Xr193t27d2j1//nwpNKPlwPLu3btHCxzHGkyw+Msvv5RB21hZY9rPGbe1GLeyYE2yt/Ttv+w7/JDtZAImYAIicPZBLkFK38YoEC350CbbIuOc2/CQOcUglwf0nACVABS/5siI477knIxyD3X94MGDHZ9TSK9fv94H3ARwW0rYe+3atR32HypNHbe1GbewYM2Q2Mu7Aln2n6X2+kONifWYgAmsS+Csg1yCE37NvNTG5yC3fzKeYpD78ePHRV4DWCrIXXpO9o/I8rX8wHAKryfgWUtwtDyB5SQSPBLoHiJAnzpuh2I8hgX7Of7kxA+h7EFOJmACJiACo4PcN2/e7H+S5qHPT9TabBTgUB6DQd2Tkwg6nj59ug88eODfv39/3x5ZyM6JjYt3FelPwEr/mLiXDNrwBwnIJdE+6o/Bbpcfko0M2YkM6Ygy1FY5pw30wRd4yHbsFyd+9S1/aBsTNkWb0SlfyOkr2fTFLsqUpC/6zDV9SMiHT/QBu7inLyy5pg+5dNOXa/rKdmTKJ+nXHGh50KAPGbIvjz0sxZw2XMexF2vsIcn3yJo2shcZfGiHfeKATOzgg48VI+SrLz5LJmMgRpJPriSWsSyOL+XiHMdB/cmX4BDlTb3m/UleS4h+HvIUsrKbVySwCduqxFjyGoPs5FfjBOh9fSo5a5WJ6ZqvekjH1HE7FGPZ2cJCe2scF9YJa9vJBEzABCKBf57IsbTjmgcxD2k2FAU9bJ4KaihX4KKAhCAsBp601cOfIIEHUQwckKFEP/WlHN0EGAos0MG9Aj1kYQ99lNDFJ6YhP2hLUFX5mmVFudiDbtkgXxSs4a9YsSHTTpxoy702atpxTx8S9eqDDnzFFrWnHSwkT3aIDcyQyfjIB8rEDBvpQxvpUV/0U0df7KCffNJY0EY2k/clZEW22IOv4kUex5V7jb38I5cN+M6Yyj/6xhS5Uq52yMRX/OT6//7v//6uEyPJQYZ40z/qVhuV6Z68KkNOlI9/GgeNJ32X5hDtmno99VfeU/V19VNQ1PXaBIEtY8aHNm/fvv3hh7Iu2Ycsx7ZDBN1Txu3QjFtZsP5Y7zGxNzmZgAmYQCYwKsgleODBq0SAw0OEB7kSwQftCBoUiKhOuYKoKkAiACLRN29cCgQUfKFX7SUb3dGeHFDQbsgPNlD8qnyNwYl0xly+sRErcY28qkxBDf7ShlwJhlGf5OQNnvb0zSwoi/1pV/GgnYJp6aZMtlEG08hVY1H5FMskT7n8jGwZT/TJdwXB6kOuftEGsY7zSGWxb/aFOjjk+aU+XYwiD8lAtnypdFdllXzkZTuX5iD/puYKelpO26bqaO2nU1md0lb9sFffAHEKNlc2Ytfar39MHbdDM25lkddtvGfvqfbHir3LTMAEzp9Ac5CrAIsHcf7ETQZkChIpjwGIcFYPfuoIPgkASGqTdXGvYI5r2vWlHFC0+EEglX1CB/pkX5dO2R0DPemsyir74YeerK+SIzsiO5VFVirLPCivOGbd6k9Ap1Nj2lQ+xTL1Uy4+uq/yym7aEfRTp1TJUlm0gT6Zc8VBcqu6SobmuXRJt+SQV2WVfNpmHdxrrkeZUzlEGVOuD/V+ZottOqntC3KR0xKotehbq02rH3P0Tx23VtuWYtyljz2HtaCPfqjM5aqfw8p9TcAEzovAPxHDgF88yNlEWpMexAoAYr/qwU99fPjThvu+hD2060tRJu1a/Mh9JB99QzbJt+i3dFZl0X7qCa4JsrnOdlRyZFsOuPK92mWZlFccs6/8sKLglpPXqSe54iN7qhzd8cRWbbCdOqVKlsoi68q/ioPkVnWVjDwe0i055FVZJZ+2WQf3S3KIdk25JghZ+9Sx1a6ugCj3n3qKmeWsdd/lB7+6Z/z7PrRpSVPHrcu2rHMpxq36sn7fm4AJmEAXgX8ihq4W/y3XA53gJqdcxoNd7zlywphPc6sHPzLjr+fVJvelnfR1BQEEeEo5oGjxgz7YnRP6qOtLsjsGWdJZldGepDYttkc50RZO/bAPOwmWxSm2yTyooI9hrgAAIABJREFUo73sUNvsK/0YH42H7I22VGWSp1x89GqCysnlO7rRlRM2xHGRrNhOZdGuyr+Kg+RUdZUM+FIuX6Rbcsirsko+bbMO7pfkEO2acs2vkw/1bQBD9rUGRLyLe/v27YO89zpkc1Xf6kfVt7Vs6ri12rYU41Z9rX67nQmYgAk0B7mgIsCIgQ5l/MqIB7kSgYremaVOfVRPXj349asnBTp6BzP/upbAQsEbwQKBQAxodOIofVVAIZsUsNE2+oHOLJc2lCGvL8m3aFMV/KlM7NQvys62q0+Urfb0lyyVVXmWSRv8yn2zr7lNZUtVlm1Qm8yROaNgkdNL9GU/mXtxPlTMVBb7ZtuxqeIgW6u6Sga2xNdapDvOK06/6RtTJZ/6rGNpDtGGKdd9f7ykd1/J3717t/9Dr1YdBDdV8Nz3a3b9ipwAritxwvjHH3/sv12hkq9+U23vshu5fbZLL3nre6ixz9jrqeO2FONWvodgMZad25uACWybwPdP3wFfCER4EBMk8vDmw0NeD3UC1PjQR5we8gQEaqdgQGWU80DPp1Z6yCOTPrSPwZECJtlDG2QoWEI/MqhHh4LjIT8UnNNPwZJO7ZCvsgoXNsJIumgjfeRKkkf72EZBvliij37YJDn4lBNcsFcBFG3gIXm0hwEs45jph4nIlXb4EMcD2dxThy3yE5vkl+zTfbZR9+hCPrnGVRxog03Shy4SMinTPWWaH3E8NN8if3GhD+XigA1xrlCvusiIcvFQe9mje9rAmnb4gk3YIhupk+2UYRO6ZCdy6EudEmVLcpDcKXlf0EawRyBDUMnn4cOHzd/9ilxOWsmVuEYGsqp62lHHtxJUv7InWMKeP//8c9+fYO3GjRv793NfvHjxna6ptnfZ1WK7/CTH/jW/XQF7CPDxM6ch35dgPKQj2rQ2i6jL1yZgApdBYFSQCxIe7gQAPJAJUvSQ56FOGR8CF1IsU3vKqedegZKueejHxD2BAg/6rjYECbKHIEz2SI4CBdrEui4/Yj8FY/RFD3Zgc5Sj9uRqj6188DOXcS//1Y4yfFVbcgIi+U6AVPWJummDnWIl2bKDtrGM6zw+XWX0RT6y+cAO+7iW7UP2RVvxVWOPDPzMCcbigV0EfwoSaRvrqK9YU0bCdtrIVq7jBw5KsZxrJcZfASvl2R61UxvNN9mFDUp5TuZxwE6lJTlI5pQ8Boq5P4FMFWwqyMztuSf4on4oKI5BY5ZDMFsFiLDm3WEFdgrW+K+IP3369J2YyvYl7JaPCta/U/rfG9mFH2ulKeMWbZnLuOIb5ev6ECyky7kJmMDlEPjnKX5AnxUQHVDl2asiUMw/JOA0wVkMms4ehB1chUBfsEKAwsmpTnNlQEuwSBAbT3Lzr8hzvWSTU9d1Shnb9V1Xtk+xGx1jbKc9TPteo+izu7VuyrhF2XMZV3yjfF0fgoV0OTcBE7gcAg5yz2CsdVpYuaJT06rOZSbQSoAAliCuL3Hqd+/evf3pLO36gkVk/ec///kuwJVsToUJepT6At2lgqNo+1S7sbfV9rnBo9gM5VPGLctcgnHkm+UfikXW63sTMIHzJ3CUIFe/ao6/fj5/1Ot5yCkuv0In51ffBLb8mptTXF4LqE5417PGks+BAIEHv94nGOXX/7///nvpFn9ZH4NfghkFqNR19aOcfjmA5eQvvtOb7ysj9HpCtKNql8u6bJ9iN7Kzrfle+nllgv9uOL7Corq5+RLjVtkwhXEX3yh/TRZRj69NwAQuk8DBg9z8zqPem7xM/Mt5zXuyvJMsvlzD1gHucowvSRIBGu+78m5rV6AKD04uf/31132ARx9OcmOwmV9joA1lBMOk3Ie+8TQ433eNAcHSkydP/j5F7moXy/tsH2s3crOt+Z42+Iud1K2RkL/EuFW2jWXcx/cQLCofXGYCJnBZBA4e5F4WXntrAudLgICKr+jiK8MIhrsCYoJk6vgBjDyeYOaTXILf+AdiuX4pmi22j7Ebuw5l+1wGLb5vQcdcG93fBEzg/Ak4yD3/MbaHJnCyBAi48ulvNJYTT4Jegt1TSkN2Y+up2n5KHG2LCZiACaxJwEHumnQt2wRMYJBAVzAY3y8dFHKEBl12Y8qp234EXFZpAiZgAgcn4CD34Mit0ARMwARMwARMwARMYG0CDnLXJmz5JmACJmACJmACJmACByfgIPfgyK3QBEzABEzABEzABExgbQIOctcmbPkmYAImYAImYAImYAIHJ+Ag9+DIrdAETMAETMAETMAETGBtAg5y1yZs+SZgAiZgAiZgAiZgAgcn4CD34Mit0ARMwARMwARMwARMYG0CDnLXJmz5JmACJmACJmACJmACByfgIPfgyK3QBEzABEzABEzABExgbQIOctcmbPkmYAImYAImYAImYAIHJ+Ag9+DIrdAETMAETMAETMAETGBtAg5y1yZs+SZgAiZgAiZgAiZgAgcn4CD34Mit0ARMwARMwARMwARMYG0CDnLXJmz5JmACJmACJmACJmACByfgIPfgyK3QBEzABEzABEzABExgbQIOctcmbPkmYAImYAImYAImYAIHJ+Ag9+DIrdAETMAETMAETMAETGBtAg5y1yZs+SbQSODKlXnLcW7/FjOX0rGUHNk8R96cvtLv3ARMwARM4PQIzHuqnp4/tsgENktgbrA1t38LuKV0LCVHNs+RN6ev9Ds3ARMwARM4PQIOck9vTGzRhRKYG2zN7V9h/+uvv3aPHz/evX79el+9lI6l5MjmOfLm9JX+ofzDhw+7X3/9dQdPJxMwARMwgcMQcJB7GM7WYgKDBLqCLQKjhw8f7qjnc/Pmzd2bN2/2AdOLFy/+lhv70+fWrVv7z5cvX/5uM+bi06dPu7t37+5evXr1d7eoQ4XSJfu68mvXru1kSyVH8qbklbwp3Kbojn0YF8Yq+qr658+f73nCNScxZMzEKLfxvQmYgAmYwDgCDnLH8XJrE1iNQBWoEfAQMHGaSiBEIuee9gROSrG/+l29enXHKWJMnCgSTElerNO1+usEV+VRh8qUY0u2iTr0oPPBgwdqum/3980CF9ku2T+W2xxT8P/GjRt736ogF9nwrOpkbzVec2xyXxMwARO4ZAIOci959O37SRHIgRrGEThVQRGB4717974LYGN/BU1VX4LNoSCXNjEoFaioQ2XKsbUrSCPQfvv2rZquHuRO5fa3gTMu4FZxl8iKbd94qZ9zEzABEzCBcQQc5I7j5dYmsBqBKoDsCxw5HSU4Uor9CYL1ugLXUQ7XBFoEnrz6kINZyjmRzCfA6Ik6pJc864t11XWXnKptS1mWF/3N/fu45bZT7oeC3IrvWH5T7HIfEzABE7g0Ag5yL23E7e/JEsiBGoYSEHE6SjBavcsZnYn9c9CEHGQg648//tjxLi+njdznVxII0rpOeqOOqFt2ElwqEYATUFapS07VtqUsy5M9Y7m16BpqMxTkamziDxcq6+I+pNP1JmACJmACPxJwkPsjE5eYwFEI5EBNRhA4Utf1KoDa5f4EUTGQoh2BMoFUV/CnYCv369Kh8nxyihxk5ABa7bOtKp+aV/Kmcptqg/rhd9/rCrSjTQ5oKePjZAImYAImsAwBB7nLcLQUE5hNoArUJJRvOCDIpU38tgPVk+f+BHl8SDrJJfj6/fffO09y9W6o+kX5lQ7KFBijP376gvJsa9Yz9r5L3hRuY3Xn9i1BLnwznzheWabvTcAETMAExhNwkDuemXuYwCoEugI1KdOv4GlXnZDm/gRNaheDKq4JxDjV5UQ3nh5KB22qlHXQpupDGX8YRwBcpUoOdlA+9Klsq+RJr+yjjXiojrzqO8cW+g6d5MbxkC2UVfap3rkJmIAJmMA4Ag5yx/FyaxNYjUAMtgjMOHHNSQFb/lU37WJ/7gmYqqCJIKzqTx/JrwLJSgdlVcDGiTDv/nalbGtXu9ZyyVuCW6vOrnZTg9yu8erS43ITMAETMIF+Ag5y+/m41gQORkCBGgoJeKpAU68GVCeFsX+f0fwxWFeQq9cVCNSqlHXIni55lQzKspyudq3lkrcmt1Zbpga5rfLdzgRMwARMoI2Ag9w2Tm5lAqsTUKCGIgLcKnBUEFrVxf5TjVXQ2hrk6uS3q32XHUvYGmVL3rG4RVtagtyWNlGmr03ABEzABMYTcJA7npl7mMAqBBSoIZwgiPv8P3ZRnv9gScbE/gTDBMK8c8v1mNQVKCIj6iAglp3VaxF9OqOcvnatdZIne6Zya9XX1Q4m//rXv/bv5PIDQJWqHyTmjFelw2UmYAImYAK7nYNcz4KLJUAwR3A099MVdI4Fq0CNgIfvsSUY4tsB9F/FUn/37t3O78tVf/TOCZoIzob+M4iKXXW63MUg2trVZkw58pbgNkZnbkugjx3xQ9CdU8V3znhl+b43ARMwARP4/wQc5HomXDSBGKwRrI45kdR3zi4d5E4dkCUDR4KzKkBbSsdScsRqjrw5faV/TM6cq9iOkeG2JmACJmACwwQc5A4zcouFCPBg5wF/aikHul2/Zq7s1q+exwTHlRzK5gZbc/tHuzhZ5I/b8nfyLqVjKTmyeY68OX2lvzVnnlR/NFj1Zx7yAxT26dM6z+jLqyr0G3PCXtkxt4w1wusjrbbP1TenP9z4w0xsPlaaMnZrMO5iwThqPirfwtgeazyt97gEHOQel//FaCdo4qHLxnmKaU6gS98lNnkeGHPS3P5ZN2PG6xHxob+UjqXkyOY58ub0lf6WnB8Y+l43yTJYKwS5Y38wZC7SD33I6Hr1JOtb457fduBz/mFpDV1LyYT3mHFaSi9ypozdmoyHWGAv62eJ/W9Jjpa1PoGXL1/uPn782Kzo6dOnu8+fPze3X6rhvKfqUlZYztkTYBM89onSEGROm9iw+Yx5BYFA4u3bt0PiB+vnBltz+1cG5hOipXQsJUc2z5E3p6/0D+XMkfjDwlB76qcEuVP6tNgypY1+G7DFAAibW0/cp7Cp+kwZu0Mw7mNBHetni2NcjYHLhgl8+/Ztd+fOnR25EtcEsT/99NN+Ply/fv2HH2xpc//+/VGBseTPyR3kzqHnvs0ETvVVhezA1EA3y5lyPzfYmtu/xealdCwlRzbPkTenr/SvkU8Jelhnhw7OunzHFj5bTYe2f8rYHcrGLj0Ocrc6u6fbTYD7/v377wQQvFL+7Nmz74JdTntjItAlAD7kia6D3DgCvl6UwNevX/fv4v3yyy/7k1Hyhw8fHvV9txYH2dAJfPiMOdFtkd3XZm6wNbd/n22qW0rHUnKWsGtpW2TT3HxskMuvlvFl7OsNc+2s+mP7MV+TqGwaW3ZIH6aM3SHt69LlIHfsrNp2e147+vnnn79zgoA1B7O8xsBeRECbE4EwAfGhkoPcQ5G+MD1xU2Qj5FUFgl5ygshTT8cIdOcGW3P7t4zJUjqWkiOb58ib01f618hZQ0Pv5PI6CWsKH+Ln2GsM/X2vJ+Eb771GO/lvrPG3r98anLtkim20savtlHLJj+PGdau+QzKWrdk2B7lTRn67fQhaCVJbEoEsry/kpAB4zPu8WcaYewe5Y2i5bROBGODSgY1Rp0tct/w6lXZ586/uJbfJsBGN2NSjDYc80R1hppueMYGWIDe637q2Yp81rrsCIulSYMR61rrifWX+gOrUEkwPEXSPHbtjMK5YaCzJnc6bwJs3b/bP5PyqQpfXBLmPHj0qq1n7XXVlhxmFDnJnwHPXHwnkzTd/q8LYzfxHDYcryYEutjuZwKEIjAly9QdIpzBHZcvQD6D86pMg99S/dUWB+FrjLl5jxk59DskYXZmFg9y1ZsXpyeUEl+C05QR26N1bToTzaw9reewgdy2yFyqXTS9uhNxr884B8BYQyeZDnOZsgYdtPByBMUHuKQUbrXZvYW1VgR37WfVbpVw2FIBqJk0Zu2MwrlhMsV1+O98WAU5mmeMtiYC472sDx8hq0dfXps3iPgmuM4FAgAdAfB2BezZCkjZm3YduJ3uJrTFoX8PQ/HD0/ffvl14ij7heWoKlvO7WmKetMrXOW+ymTdwvWnUcql0V2C2te8rYHYNxxcJB7tKz4XTltQamnPT2Bbh42CprCRoOcpegaBl7AvoVGps2ifvbt2/vc+4pbz0RpW1LcNPyIN0bM+EfPUi2FJRPcNNdTpSA5t/QHM/r7tjutNpDuydPnux/iDzVNVYFdkvybWWVdbb2W5JxxcJBbh6Z871vCUwJcHl3dyi1yBqS0VrvILeVlNsNEsgbLxugAl4e2Fv6SiH9KnUowBiE4gYmMJFAa5DbF2joe5/J3717N+o/LUFu1ymr1jptctLa0drP9br/448/er9x5Ri2yzbl+NDFQG3m5FPH7hiMKxZ99s/h4r6nR0Dv5Hb94VlrgItnvI9bfb3YGl47yF2D6gXLjBsh12yCPKz5jlxyEg/IU/++XGzn4zSPAKfxS6Sl5Cxhy6FktAa5cc1F21h7BIoERHxYc6y9lkS7+FsY9Ylrt6sNbfnhsPqtDeXYy2mP9gPuacs937JAWtr2aDfy+2zfG7Db7Zlh15r7wJyxOyTjrqA6B7lqxytefCWc0/kQILhlH65OaqnjdJZAOH74BoX8HboQQY6/XeF85sZFeaKHaf4PIE7x64G6Bqbr4dHV3uXdBJYKTpeS023p6dW0BLkEa5w0MmdzIgCpArQ///yzM9hFHvVax9x3Jeq6fljF9uo3N9hJABTf2cNOyhSQo++YtsvfLh9UPzefMnZRZ5d9cxlHHbru0tUV5LJe1zwBl13OD0ug+p5cfbUYY159+KaFmDjxpV3XiXBsu8T1MscsS1hiGWdFgI22esCyWfJAqx7KpwBAD1zsdJpPgM1sibSUnCVsOZSMlrXCfO0KJghUCRxj8IjtLUEuAVg+yc32VG0iG9Z/tQfENl3XS9qe7UbnkO206drDumweWz5l7LKONRhnHdx3schBbuzLqTycnc6HAD+czv3qL052OfU9VFrmCXQoa61nEwR4QPFrPjbAKrExd9VV7Q9VpofhFNvou6XT6kMxXSo4XUrOofxeQo/mY98PhKylvnrsoP7evXv701nu+4JcdP7nP//5IcCVP3nt9gWL1BGAx1NbyWnNl7I9243+PtvZA7p+eGi1fajdlLHLMtdgnHX0saCOtZn3TOaRX1fIJM/j/v79++UrCy3ecapLkMx/BXyo5CD3UKQvSA8bHF/wzgacU8uvQXOfQ9wrMB8KGLps8alFTaYKTsWaur5PDDKiHPXnB6lqjtWWbK+0CnKZnwRH/BfZOqWtPHv79u3f77xSTz8FItR1BSCUo7cKAPPazfeVHcjhv+9lfdC+JS1te2VnVSbbCMqxeekfWpcYO9kY86UZR9lDLHKQC1f+oLB6bzPK9fV2CRCochLLawdj09OnTyf1G6sntneQG2n4ehEC2swrYTxA46lS1eYYZQQOfKakvqB+irxz6hOD0+wX84R68ph4UBIUxfGIcnioEwDz2gvszzXhGz7iuz7/8z//sy+j/MWLF52BI+wUWMKTNRdZKUCmjkROmcai6kP/uHbzfdc4SLaC7K52Kl/a9srOqgz9lIub7FkqJ2Bk3OaOXWXP0oz7WCiw1Zwkbx3bynaXbZMArx2MOZEd234pKg5ylyJpOU0EeIhyyrv0KUmT8o5G2MSpIA+KMYnTNAINHlpT+o/RtdW2PAC7EtxhR2CRE2Wc6ClFOQpy40mv2jn//wErp2l8ZZiCqurkljLqYUseXyuAcX4nN6/dqs1c/qzBpW3PdmPjGrbP9Z3+rf7P0XUIHXPsc18TWJJA9xNoSS2WZQInSqA6leChP/YTTx1P1NWjmBWD02gAD1p+MGj94SDKGds36vV1GwEY59Pf3JMfRLpeS8ptD3m/ZdsPycm6TOASCDjIvYRRto8lAR7SOskaG9Tm9pwWOf1IIAansVbsIzdO1/hVcZWiHAe5FaHly/qCWMaKAJc2p5i2bPsp8rRNJrBVAg5ytzpyttsENkAgBqfRXIJbfsBQkETgyml417t9WQ5tfXoeifraBEzABEwgE3CQm4n43gRMYDECOThFsE5iqYufGPRmA7IcguR4Cpzb+94ETMAETMAEHOR6DpiACaxGIAenKOL0loA2BqmUxb/czwZlOfTtOvXNfX1vAiZgAiZwmQQc5F7muF+E13wlEsEROX9pHv9afwgAQRR984dyTiL5C/CcCLqqv/jn/UXKLzEoy8EpzGCYT21hVDEV4ywHlpfIUzycm4AJmIAJDBNwkDvMyC02SIAAiOCWgJTPw4cPR/3HAfTJf13OaaO+iin/r1EEafkrlyhDL7Kq+g1iHW1yDk5hMeZbFaQwy1G5cxMwARMwARPoIuAgt4uMyzdNgCC36w+TdMJL4BQ/nDAqEZQ+efJkH6Dynb75r/4JeOPJ8FAgHQNe6biEPAenelWha2y6mGQ5Xe1cbgImYAImYAIi4CBXJJyfFQGCToJZnebKOYLfGMyqPOcKxhQE51+NI59XIJTySa36S1euV79zz2NwCjOCW8oyzyEOUQ4sOQ0+xe9oHfLD9SZgAiZgAocj4CD3cKyt6QgECDLjHzTx3ifB1lCinwLU/GqC+hLkIqsrgCWgi8FcVzvJO8dcwSksuY6f1v8IAi6Sw7WD3HOcKfbJBEzABJYn4CB3eaaWeGQCvEbASaoSAZaCzdYgNweokhVzXmPQf0+cA1iC3/gecL6Pcs75Oganc/xcSs4cG9zXBEzABExgWwQc5G5rvGxtAwECVN6hJbDkE/+AjGBXJ7RdonLA2teOU15S1kOQHU+Q832XzHMrXyo4XUrOufG1PyZgAiZgAt0EHOR2s3HNBgkQbHJay6sEfE0VH30jgtzp+8MzvUtLUEWwPJR0MpwDYwJp3hntOukdknsu9UsFp0vJOReu9sMETMAETGCYgIPcYUZuYQKDBPJJbu5A8HyJfyi1VHC6lJw8Lr43ARMwARM4XwIOcs93bO1ZQaD6AygCKD4tJ7eFyL+LugJZTnkJcKm/tLRUcLqUnEvjb39NwARM4JIJOMi95NG37yawMoGlgtOl5KzsrsWbgAmYgAmcEAEHuSc0GDbFBM6NwFLB6VJyzo2v/TEBEzABE+gm4CC3m41rTMAETMAETMAETMAENkrAQe5GB85mm4AJmIAJmIAJmIAJdBNwkNvNxjUmYAImYAImYAImYAIbJeAgd6MDZ7NNwARMwARMwARMwAS6CTjI7WbjGhMwARMwARMwARMwgY0ScJC70YGz2SZgAiZgAiZgAiZgAt0EHOR2s3GNCZiACZiACZiACZjARgk4yN3owNlsEzABEzABEzABEzCBbgIOcrvZuMYETMAETMAETMAETGCjBBzkbnTgbLYJmIAJmIAJmIAJmEA3AQe53WxcYwImYAImYAImYAImsFECDnI3OnA22wRMwARMwARMwARMoJuAg9xuNq4xARMwARMwARMwARPYKAEHuRsdOJttAiZgAiZgAiZgAibQTcBBbjcb15iACZiACZiACZiACWyUgIPcjQ6czTYBEzABEzABEzABE+gm4CC3m41rTMAETMAETMAETMAENkrAQe5GB85mm4AJmIAJmIAJmIAJdBNwkNvNxjUmYAImYAImYAImYAIbJeAgd6MDZ7NNwARMwARMwARMwAS6CTjI7WbjGhMwARMwARMwARMwgY0ScJC70YGz2SZgAiZgAiZgAiZgAt0EHOR2s3GNCZiACZiACZiACZjARgk4yN3owNlsEzABEzABEzABEzCBbgIOcrvZuMYETMAETMAETMAETGCjBBzkbnTgbLYJmIAJmIAJmIAJmEA3AQe53WxcYwImYAImYAImYAImsFECDnI3OnA22wRMwARMwARMwARMoJuAg9xuNq4xARMwARMwARMwARPYKAEHuRsdOJttAiZgAiZgAiZgAibQTcBBbjcb15iACZiACZiACZiACWyUgIPcjQ6czTYBEzABEzABEzABE+gm4CC3m41rTMAETMAEGgh8+PBhd/Pmzd2VK1d2t27d2v31118NvdzEBEzABNYlsHqQ++bNm92jR492d+7cWdeTjUr/9u3b7qeffto9e/ZsFQ8+fvy4e/r06V7HKgo2IhQO9+/f3z+E4Q2TqYkxe/Xq1e7nn3/+btyY6+iYMtcZ/+vXr+/tQy72Oo0j8Pnz593Lly/3HN+/fz+u84m3juu41beuebq0q69fv95dvXp1vyYIdm/cuLEjn5JYB6xPbD/FdEj79OzkB4etpWovZA4TCzC+W0usOcZBa4+9Rnu2yiqfWttVfVvKZAO2+blRE1t99TABeHhPefDXJh+u9MGDB/uJzQTq+ly7dm335cuXyUatEeQSgCnF4E5lp5qzIfRtGFPtRi4bKyzgTYDLeHI/JSGDTRwZbDJKU+c69hAckyRD95I9NY9zYaqMY/Zj3+hae7GccdADhfI15tExOeCb5m2rb13zdEk/CGYJcJ8/f76IWMZxySB36T1lafv6oGE7gQvzeWtJ+1h87m/pWZR540/cV/CFgD2W5T7cr7kn+blREf+x7CCrh4keJ/uPZpxuye+//77fxKuTCTZ2AuFTSiyqzJqNeQsbJZtG6wN8DPO1/IcpsmOaMtcrOVHmnGt+wNxyItiPc0JjGcs4vdU45IfRln3Ptle+5zbV/Zrzi/1v7g/6lc1Lla21pyxl35Ac9pMt7N2VH9VeqDlctd9amQ464l5U+bDWnrTmut76cyOOg4PcSKO4JpDteseMwPft27dFr+MUcXLDT/5bDHI5cWTRDm0YU8iutbFWm0y1sQ/ZXMkZ6tNSr5OGlran2kbBq+zTWOZ5ohPrtR4o0n/MvMv3IZvWml/sjche6hR3yI+x9WvuKWNtmdreQe5Ucuv3a91rWtuNtXitdX0Oz43IsjnIBag+LDwNnMrISbFcDyg9+AnCBJCfFDh1jCnWI49THMpItOVW+9E/AAAgAElEQVR4Xr/KQibX6CPx6wNtCJTTdm7ijycIcONpLWW//fbbIn9YgW9sxNgtVthMefSFa/nZ55N+taUxoR9JD0ddw4cPP4nmRFvqkDHmHZ8Wm7tk6ydi2U3e4i86NSdkb/QpzsUoO/tc3WMrc5R+XfOJOtrFBHNxj+XVNe2iXVkevsgGcgVyktVnI1yy7KhPfJGhdiqbu9awM84h1rwSY0bd1NcxZK9slVzlGnPyyC+PE+1jfcVXMnOOf9gPz7jvcI9/+m0KXJFLm5iop7+4c53b0B4ftKaRo+voO/q0pyIPWZTFRHnlf2zTeq09UbYrj3tkq6zcDgZaz7Gubz7FdvGasZVtyuGGLMYJHpILMxL647jk/a+yD5nw19hrLKpxpz/tsKdrT9E75bRBP3K4bk2MvRhKBiyU4tqmDA7YwgcerQmZmo/oqeYdvvKJCX2t/qBDbGEnfcjkPqaWNUUf8dR6ijLinokOxoKEbI1LXHtc4wu5+sJR/SQ7tlMZOf5Fe1r5Yxt64yeu7yG5tJXePA/lR5Qd9cl/jaP8FyfNPeYh/ZCvPi3zP/JZ6rp59TDQAiPllOEEky9urECOkx5nacPg0079mMBKlKsNZYBBNn1JANIGBGB00J5ck1cw0QP8OPDSMybnXVt+FccfVihxarHUyQUcsD/bip9MFhJt8FO+yY6uHF5ipjaakHBBniYg4xkT48FHY4RexoD7oTRk85Bs/IsLZkgf9dgne7GRa2TkzUL+t8ikDf2jLerPWMWUx426in/sU11XcrAB/zRe8k3zoMVG2R11am1IDnW5bM5aoy9zBrslO85Hximu62hby7V8ivbHfppHrB+10cYtm2g/xDfKjNfYLx2sH+SQ8JtxxFdspB36aMPaUKIM/2FO4l7rDBlKkqd26KRfnJfooG9uE3kjr5pf0jMnX/pVBXzUHi+7huaT2lW5xomcBC/tt+iBG3NDvDRWcezgq1TZRxn9aMcaRX7Uob74QTvZgu48LpQxxrkN7VqT7MAHPto34jwVY3ynXD6guyUxZ6Pt9OceeTHBVWxVrvWr+64c25GLTfKJe/WHpVLrmkKW9nDJlgzYY2vkhi4SYye99FPiGr/hCUdkYytl0kNbtYt9aU9bbI/jFNtIT1cex0BthuRSTz/pkV/RXpVJJrnmq/pVZXDS3EKGeJC3zP+ob8nr9tUTHv4YrCQgDJQSk53BU2LyMKAx5QUARBZkTMhhQKRPuqJs2muSxb7oa120sV+8JrhFf/7EoDe2n3qNfHxTgg2TUQl/4+RSeZVnrrQRtzhGKpMMTULdkzM5sY2xGUp9NrfIxj90tfqpxRp94prNL4979nXIF/yNMpCbxwgZVVnFf0hfJQf9cZ7LBuY6qcXGyu+Kc1WmvtEG9A6tNWTFBxB94lzeGz/jH9nVNU/kS9y0VRb7DPEdMpExY6xjqsaeMtoqscflvZD1QRuNLW1h2LUfyo+WPRNZ1fySPVNzHQAscYIbbdD4qgxfp86natyRC4/MlnLmRNzr9PyRLeTZPsoY4zymeS4MrRvWGXbltYJcyltS654oH+LeqbIWPZqv5ErYic8xZQbUjdFD+4qtZIhV65qCY9zP1F925TkR66u5VJXBlHkU52zVbu7+g834A4uYhuSe6nMj+rD0ddvq+a9WDWCcDIIm2LSJmzVdq8meJy/3DFr10WTT5M4Qqj4qi5M69xu6ZwOP7+Pyq7p79+5N/nqcLn3YKn60iUzjRtTVP5ZXrCtuKpN83YtbzON4R13xus/mFtnVRhDl52v8jBuJ6vVgQp6S9Ou+NYcNfqEnjxEyqrKK/5C+LEcs4hjoOvvcZ2Plt2RHPlVZ1Vc+y5acs9awBxvhEHUMMWitl11dsitfVKbAV/fZfu4z3y67aIuPMVVjTxltlbiu1lMMZhREaN9T3+y7ZCMzf2Jf6ui7ZNIBwNI/8MtH2TpnPmmcyWMa4kF7xkhMY99sH3Wt4y55OWfdsM9Qnp9XGuNoQ9c1bav5m/fEygeVZVZdulTOPJON5DFxn8ukJ7bru65kaH0giwS3oTVFO+IS+MS1Id34TYCInDwGtKEePZFPVUZbzZ0oO/ZVP8rypxo/yck5fcWAujFyT+25kX1b8v6f3bdRKgPIZACSQLGINDhMoDyJqolKGYOkxH0cMJXHvGuBICdOvthn6rXePcsnFX/88cci7+NGu/JkpQ5/YALrIS5RVsW64qYyceOevnNSl80tsuk7ZhzFJtub/aJeZblt372CduY2m141RlVZxb9PD3VZjlgM9RuysfJbssmVqrKqr2yNfSUj5uwL9GfuwiOe+MR2U65lV5cNlS8qoy9J91P0qw9jltdLNfaU0VaJ63wIQF1sJ/vIY8q+00c+xXb5Os+vXD/lfulXFWSDfNQ9+dT51MWxiwfrHKb8wMEzrLKlKqMPn5i4z+OexzO2r+RSn+XEPvmatqy5nCRb+nUf26lMbWJddU07nvnMZa7RzSemqkx6Yru+60oG7eMYcj20pqSDccVuPvqhV3WMvwJUcuadEj6iJ/KpymiffcztdC/ZU/PIABmtck/1uTGVw1C/f3bfoZb/rddPUdoEmBixLE90ulUTlTIGSYl7NpecmGgMHilPHrVFDsFITnkS5/q+e33/49InFZXOPFljG3xg46r8i+10XbGuuKkss40LWzLHcsw2S1efbC1Q2SPdXbnmT5YpXTGwUlmXrFxOe5hHGdUYVWUV/yw/32c5YlFxV1mLjZXfkh05V2VVX+zG1mouyq7oG2PDwweW7BNLJNkV7Y9yK19URl+S7iubq7IoX9dwYKxjqsZe81Tt6Fftc7RTgCL7eBjFlH2nTyUL7shQyvNL5VPztV5VwB75WNk2dj6JY2SB3IoHsuFPcKNU2VKVtY5737qR3LjnYEeeP7KtytUWX2LKsnVftcmsYhtdi2s8zOpiQHlMle5Yn68rufgXx7BlTWW5sqNa74wB6yquLfkc+VRl6GGctZa5z+10X+muyrLtuo8Mop5KhsrwG9viPMtyxEZ6ouwh/6u+9EdH3/yPupa+/ifKHCFZEyAaTRk/HcUyiawmKmU4rsSGzn1cONQhT4u2C6AepHHg6Bs3LOlpzQlu+ZLz6vtxW2W0tqsmWezb5Xdso+uKddVfZZq0sMOOzIzFoQUiHVWOvJgkn7IW2Vr4sifKqq41X7Je5gvzMCbK4lyLddV1xTCPEf2qsqpvpUNlecNWORsRa0pzn3ICRflb6cn2xDGQXHGOY8o1fSP7qi8yhtYaMqIc+mS7ZMuUXHZlHZJFefZFZWJH2yG+kteVo4MxiKkaE8poqwS/bB91jLXWHuNcyc++aw307ZnIRlb0XbZMzdd6VQF75KNsY+zyWLf6o3Fv6V+1zbZU9lHWOu45uKCvxpwxrPzK80dcqlzzIY913hP7/MqsKj1V/y4GlMdU9Y31+bqSK1b6wbllTSE3c4myYRf32jwf8j3yqjLKef5oXLvazd1/zu25Aae10j+77wgNmmQxqNQC08STOAaDQWdQ4yRiU2dRq0ztKGPyMSGVS5YmM7piUhBFXyYXfdHZsmCjHF1zUsG7uPF9XNUtnct2fFOS3zDhQx1lLYm2Yq1ARtwiDwV+aoNstYMdDGHZqnfI5iHZ4oBefI62Vn7ThjmEr2pLzn30iXb4w9yI87WSqTIxpD39Nbc1t2gne2mrJF3o47olSXaUQz+V4w9jpQeV5LbYKBlwwV7WpgIo2FEOb80FcjFCPsyQEZP87lpryJRs+nFPW3KSAmrayJcov++a9vSr7FI/+YxfStKJf0pq18VX7ao8MlQ9tjHueexlr/ZF+KGTcpVhC2W6R6bGhDmHbD6sMflOW+mkTOtPuezSeOX5pfop+VqvKmCL5p3m4dB86rNfvsc9RWV5nFQObxJ6xZv5oz0l26cxYPy4VtK4q0zyGSvtI9igdUE/9dGaow9t6EM7yZKOnFOPDGyRXHLuZT995IPaUKb5Fttl+brX2tEPV+TYiW7q4tzMnKUb31qSxkBMkI3MuJaRhY/o1xqiPWW6Rxf3sllytE+QY5vac097MZfPsgN58NN40o+2jC12qB/tqr4qQwe+8Mms+vioPzbHpPIuubSnDmbYqPaak9Fe/KMdvvHBV3yjHD6aM+QaT40vcmOinv7iRX/8jXMwtl/yelKQC5wMtyrDUDmmXBND9+RyFJCCxEAAQkmTXf1iHW2QwQBQD7yWxSrZMdcXnEvPmqe5mQU+kphw+CAbYBIXTbQ3XzOZYEd/rituVRly0MGEpT+6saNV75DNLbKRgV5saEnIVB/6aQGqb+ZLGzFWmyqHm/hjC3qQLaaVXOQgP35o15fyONA39mGjkB20xS6lIRtpJ7sZT23ulGtjUzk60UM5fbJdY9YasmJ/5EbdrEv8pDz6Kr+6ctpGtrqO7bFT5eTYUZWpTx9ftcl5ZceYMuTldcn6Zu/LCdvjWmQuMg/hyTiR+vbMyq6sY+y9XlVY6msUo/44bxg/7OcTy/N8iv2ra+0PsBviQRv0am5qrlJOinbIPvL4qXRQRiLve0YxprKXcWf80cn8aH2eRRnYlffE7IN0RB8o60txj0AecxBG2k/oG+Vxje+V7j491NGH8VBfccn9WtYULLSeyDWuyGIv0NiIm/ZbeER/Ip/YTzK1NpGb++KH0pT9h75iEW3SHKO+T+6pPjfEZI18UpC7hiGWaQImcDkE2Pzjxnw5nm/bU15VOOX/xnfbdG19JkBAFwPDXO97Exgi4CB3iJDrTcAEFiXASQenU07bI8CrCmuc4m6PhC0+BAEHuYegfN46HOSe9/jaOxM4KQKc3nKKG3+ld1IG2pjvCBDQEth+/fp19/jx4/3nuwa+MYGVCLBH8AoArxF4v1gJ8gWIdZB7AYNsF03ABExgCgHe/+XvEvi8ePFi8e8In2KT+5w/AX4Yju+ccu3Xm85/3Nfw0EHuGlQt0wRMwARMwARMwARM4KgEHOQeFb+Vm4AJmIAJmIAJmIAJrEHAQe4aVC3TBEzABEzABEzABEzgqAQc5B4Vv5WbgAmYgAmYgAmYgAmsQcBB7hpULdMETMAETMAETMAETOCoBBzkHhW/lZuACZiACZiACZiACaxBwEHuGlQt0wRMwARMwARMwARM4KgEHOQeFb+Vm4AJmIAJmIAJmIAJrEHAQe4aVC3TBEzABEzABEzABEzgqAQc5B4Vv5WbgAmYgAmYgAmYgAmsQcBB7hpULdMETMAETMAETMAETOCoBBzkHhW/lZuACZiACZiACZiACaxBwEHuGlQt0wRMwARMwARMwARM4KgEHOQeFb+Vm4AJmIAJmIAJmIAJrEHAQe4aVC3TBEzABEzABEzABEzgqAQc5B4Vv5WbgAmYgAmYgAmYgAmsQcBB7hpULdMETMAETMAETMAETOCoBBzkHhW/lZuACZiACZiACZiACaxBwEHuGlQt0wRMwARMwARMwARM4KgEHOQeFb+Vm4AJmIAJmIAJmIAJrEHAQe4aVC3TBEzABEzABEzABEzgqAQc5B4Vv5WbgAmYgAmYgAmYgAmsQcBB7hpULdMETMAETMAETMAETOCoBBzkHhW/lZuACZiACZiACZiACaxBwEHuGlQt0wRMwARMwARMwARM4KgEHOQeFb+Vm4AJmIAJmIAJmIAJrEHAQe4aVC3TBEzABEzABEzABEzgqAQc5B4Vv5WbgAmYgAmYgAmYgAmsQcBB7hpULdMETMAETMAETMAETOCoBBzkHhW/lZuACZiACZiACZiACaxBwEHuGlQt0wRMwARM4KIIfPr0affw4cPdlStX/v5wT/lff/21++233/b5RUGxsyZwZAIOco88AFZvAiZgAiawbQIfPnzYXb16dXfr1q19UCtvXr16tS8n8H3+/LmKnZuACRyIgIPcA4G2GhMwARMwgfMk8ODBg32Ay4ltTgqAX79+nat8bwImsDIBB7krA7b40yTw5cuX3e3bt3fkXYlTmBs3bux4gFWJhxf1jx8/3n39+rVqctAy7Ll58+b+V6WcKFUP3IMaZGUmcAEE2EOuXbvWGeSyDnltoW+vuQBMdtEEjkLgoEHux48fd48ePdr99NNPR3F2LaXaxPQuFoHGmzdv9kHGixcv1lL7g1zsILiRHV05G/KYDfcU/FvaN05VCE67EnX8+pFx7EsEt2pLkHmshD/YS2Cu4HuqPd++fduv0WfPni3qDixZ/3fu3FlU7rGEff78effy5cvd9evXd2NYweH+/fub44CP7N3Mj7UTz4qnT5/u9b1//35tdbPkK8hl/VVrjr3L7+POQnzQznFdn/Lc2+o+osFkP2HvJE75+eefd6z5NdJBg1wmDM7g1CESD/y1kzY4Ah02MxI59/h5jPew0Fnpxq5ff/2182SyYnVq/i3lG6ezXb8+pBx+Vb14xNNduPLDxbFOT3mw8oBdaq4tGeTGNcj6Z1M7pyCXBw1zZUyQeygOPKyXfEgfMsjFdoJc2C7pQ7XHLVHGfoCtHCDwh2ZO2yIQ9ynmHnP91OfeofaRNUaStc0P+iT5oful9R0m2gxWa+MKRatcMmnHPHimGkFgUZ2MEvjcu3ev/Ml+qq7WftjUdapAQPT27dtWUfvA6ZT8W8I3AlV+fcgYVQkdbHBVkFu1V5BbcaraL13GA/ZYuvt8qdYgAe65BLn4zgbNXBm71xyCA6fmWwgQu+bQFgIN2c6eoleFuvZetXV+WgQIavOepHV96uvnEPvIGqM1Zc/8f+2du45kVbZ2eQXEG4DUTwBOu7QENsbBwysDt9UWVqs9pFLbSI3PAyBeoPERNhIuOHg8QB6N/M+omjX/uS8RuSMzKvJbUvTae+255mWsuS6xM6g+149HP+S6cJ3r8J5+vPbmz2qnbjx7dHeZtUMXb01Z/NYK3/o//fTT+42SgV/7YGureOA66q3iQ+I7Jbb6ZnQpxqNiI6Y1lr7JXZOpPvp29yjmVffWNT6SM3t93dJ31POlOfi2LspLXNwMT11rLs2BLxjkxbVv0ktcaXeveFti6AfdvV+S1xjk2WUJ8Fcr/rqcQ+5lOXftrE2nrpldx977kw65bFz+hoKa5KC4GFXHXfxrW5UluUgsnqOrL2Qs0hxUeY4d3krUwp8Jqy/IU/hWZj/6VvtL/le9p177p2K+xZ/6Zyr68h8u4fvSW8Vz/amHHhZfDtznlHPju9bY4Lz1ht2Y68Hbg6w5VTewSf4c1nv7eNjXF+vq715dXY55ST4yN+siVOcr84vnfc6qa20O0o8P+pjT+M48pk8t9Tky/CmLtqWCL+jDt9oX3cx72vwrEjKuF1Wfv6/Vpxq/cviJL8jIgesuyz3Pecb6hQ+1yKG29es6Dlyjr/5Jb8mGP6HAth/4yAjbroXwgQ33xjX5zHP44YMFFrVNf5DpfOuYoH8aT/yDFc/xy2va10odE3VX3qf4uWZnz7M+N+s6saf/OTKsP7woqfP/P//5z/1f84764i1DxoXxmMaa+WPOkxe9LO3ZyqET/YwheqoO7Zt/k3310A8dfMh1/Foq5liVR5YYaaOufmO3F31BfprrVR5/tKV+nqPDdvMdu9W/ac7UdaTq0E/jQLdt+lPjgnufs6dwVOfWGtrjn/xS11H1SYdcEgwwFOCZcNyzqHSHpzYHAoDocvFGF4msrnoPOOBY6MPgI18XT5MDue4LbWv+q/uc2rdpp/yZas9h61xfqh/YYfF7yGJ7anzXHBsbAodcfFwqHlonZrDoPw1AF5tJ3WSWdB/djs3uz0NsMKeYl33+sMC66SDD/KvzbbLZdSDDPKYvc5q5iy7mZf0SS7sy9HGtqWtAt4cMfbCpbvSwePPBd9cLYsFmLTynPzIUdKCr+4Uu2vRdm3UD4XmXwR59LMSyFg+yjgP+4g8+2mfLBjzwv44R1/iPz/iLTmOm3U0UBsrpL315jk4L67tt+Ma6rI3Kl1iwgz0KMjw3FtrcK7pMj0Hb1uaP/bjHFvrRqe49fqrzobXrAb7Xtfiheqf+rFHYqbZ4oXHqC5dJd22DJTmHHWrnSeXKuFLc4+VP29aezXN0dx2eN9BVbS3lGnlAXpFzfPC1zs0ak9fI11ykHT/wh7zWJ65pM3bktuahNqydC+gxNp+hv+YxMvpe/VGeuvsOp9oPmaltazzO4Yj/rifYRQe+wKiX7mN/fuT96xVrh1YcqwMMqFomx3ubE6D2cwCFwT2LbC3VFgtY9YPEwQ6TwNLt0k5b7Vd12u/cGl0saNjYo5eDFP9x2pGlLq744eeIhfaU+Fh4jz7wHRUbh1Q+S4XNgUMwb0J68W3uFJs/zdgz9l3vufdr/pyr037kjgssbSymNTbmkYu/fXrddaiHhbCWvlCzOLoWKOcGUzdOn1mjB5u1TOuNbfpPLPTrm46bqjbxqa9LHkRlheySjBsY/vWYq8/1Gr86iz02XFONUZ3Y7f7xjDW1+idv+1HLbWpjDbZ0uT3jiU89Tn3oMWiHmj49n+ADt7of6NOan1XvnmvWpG+//XYUdW7ix9p6M3Y+o9H1mb8osrdcosiwjseUZ7Yhb9nas8kRZCzu6VWH9tfGEJmeR3XdUn+tp7loDHVNsI2asmceVjteq6f7hR8Wc5jaQp5XGdon38m5yg253rY1Hqdy3LuGGkv3x/ZL1G/uCBsWWDRYjPrg2G1yvLeZqPaxRq8DSBJ7b0IpZ4Kgt3/oY+l2ad/y377n1r4BxPb0FrDq3TpsVdm919qviyptW28uT9W/Fd+1xsbGs/Zv4zJmvJFdegviW5O1seVwfBTvrXHZ48+WjqXnff64CTF/6yaz1J/2roO2aVGmrR5UuKfv9Flae9RNn1qm9cY21xbv6xdgdPQDLBtD30Bdj9BBUdfke+07cah+e40eddu2x4Z+GaN9t+wij5/6bz9q7e5t0/bWeLqh97HVnnqqXa/xs3K1nXzimUVd3lPbtqa/yvdr1pO1n4GxDuLD9KW463rovS8Bjvp5wuTPxAt2xFgZ2oY8xXvk+qfu2ciytrDW0I6sOnim/Xul//c/tmmf2nna53PtV6+nOaHP6kXeNg++2u4xcT/lZLdZY0c3cU+FeeEcoq5l8r1zQ762Gcfktz4hcwpHWXTmfQ3V9+qPbZeqX68COy0AHBB8HGy7To73NmHYx7oPFsmOLKB55jcaB8h+S3W3q9ya/8rsqTk8Tm/7PGhuLTaXOAiis7+1ZSFeetuwFudD4rvW2DgUbm04a29k6bv00wA2Gf7FBv5dZK4fo6z581D70/xh7jEXmZPMza0y6ejzHB20IWvhfo9+5a27Htqn9cY24qkyrjHqc63RlymeLoMsfmwVZPbITTb32NAvY9SfJbtsTjzjcMgaKSP7UZ/apm300nepLPmqPfVM/eFT39gqg82aU+ryObVta/qrfL9mPVlb5z3kUj9Gwc7S+nSE/YnXNHa2IU/xfssHv0jzBp987Lmv/arHtjqG9PWLGvXWl/JpTuhz1WubcVHT95zCHCM+ago53P3EHucsnnE9+Tm1dW7or23GseX3KRwdh601VJvVH9suVb/eWU60YFD1oDs53tvs182xuE6LFQNPOxsr0B2galddta3bVcZaP2ofn+2pWeCmxctv1FuLzZ4D1x4/lNHu2qKr7J76IfFda2wcCvFtq3DA5z8IpLb458elQzLtS8/UcWQ9+UMO+O8zU//3v/896Z+Lq/6tzR/mDPORzWitTDqmRZk2ZC3c1ze7trMWMP+XSteDnPO89rFNXd73tcC1xjcs+NjXKGXQUe31DYtnVf/Eofro9cRQf9ds6Jcxqm+yix7Gs7590ob9qE9t0/bWeOqrnLWpPfXYXmv4TLmCTWKyqMt7atvW9Ff5fu0hdlpTXI/7S4eu46h71oN//OMf9y85qj9HrgkTL8euMrQNeYr3Nf+N2zZkGa96SOq5r337UttW7fscXeTGlB/KUE9zQp+rXtuMS9tr87Da6dccYPHNw2R9ri0PwUt+Tr53bvStbeqWfbU7te3hKIveX1t9bld/qv1LXL/eWXZod3AV7YBxvG58DH4PRhjqoGaQkXNAAcOnlqqHyUBy1ORCR/WvyqunPqet+6/cnpoFbjpQeviYnlW9LoJ1QarPT732DfJRB62HxEds/Mm+HhJPjafKHxEb47L2U4Ute4wTOTWN1xH+Vft7rid/aONwC3/eSD/k93l9/vS5M83j7nfXwfNpztGGrIUFkXvXA9tZW+qct92666F98tM21xgWcezRvxYWbNpZWyhsSqw91QcXcfmoqx4Y6YuuugFMHKptr7Gvbtv22NAvY7TvZHeSlZH9qE9t0/bWeLr+d/7aU0/1xWu+dMCoy7A/1DFQl/1qPL1vlVm7Zq3FNgfZmqv8vyB+9tln98+mFyFrOs99xl/rsMu+U/eAI9cEGVZeXHf+tiFv2dqzp7zsua99dVLbpk/k2jQ/fV77ej3ZNobazzbj2jMPtTHVzgsPulXGuGrb5OfUBretc9jWeJzKURb4U0tfQ3nG+PSxrX2Ovn69s+zQDBgnMwsTi74DTnc3AQJDzoEicJPFNiGipy9IyNJmH5PLe5MDfxhMPtiuyc0zgbu5bPm/A8ErERc4DxU84HBB+95v7xyO/CfEWKDOLdolcaZD2KSXPiyI+Dr97OKh8T1lbFO8cNm74SALyyoPD3hxeOy/w/OLDTJLxbw3h5fk9rZjq/+1AH+rz/iJb6cWF6z61pK5RAzMMT48c34t6e9zkH6uEXWuMtfhbZtytGnXeskWfboeZD0EEZOF9QLdrmW0cyCijbWF4rpUNwsXbOzwnKIu+mtDm66PPKusjK+vWfcKy/84DpPclg37OmbknXaJU18xp6yHQmTxFzlidv3UZu1rW81rmdiv2nUcrQ3XPviAPB99YEzkrbw1vpBndUyQp6322eOnOvfUzCt/msSawE+VWEthxp94KycAACAASURBVId/0ov2rfKQdYG5zjoAZ9ZbiusU98z/o9YEdDM25oRxuRc7b2jHH+TMJ9qUY1wYaz41rxkfnjGejL3y6IARZc8YIoucY889etG5VLSNjDmrfW3T17jw3aJPS3NduanGHr6R571o3zWKGhvkOc+Ij/60VY7o4R69+Lt0DlM/ctN4nMPR/EA3BR/xt/KiXduwe4xy0iEXh4FCAgunOslC53MDU86kY2B4xkCgh1oo6qqLrDIOtjL0UQdJUhdeZJCnL8+wSdnyX91bdV3gsMNBFVt89i5u2mAhfMj/GQSLmLatt94iY9tDLn36Yemo+E6JbTognhubbK2J9ZQ3y9r1C4NfItjE3NjUXVmucSdfmQv1MFB1nHLN+DBm+FmLXyz0uz7be41/5hG1CzALmPONdhYo59WS7j4Hq16uuy3b0Md64QYCt7rZTPb26EYH8VTZqpdr169pXcIuG4cc0OU6RN++xqkLdpVVtV9jrnFNbOpz19ElG8i66bgWr9lFhufEhu2+oXduyPS2Nb57xrPyx3d8Ys0mjyq/yoFr1v7qC3njWPC8PiPGNT+77kvfP2RdYP6zJsHHwtynzZcvR6wJ6N7DEBnY1jyjzeJc4Tntdc/m2nnFuDPejD1tfXzXxhAb9NMHrqsdfak1z5lH2ppimNrQsWceVlv9mliZS72gV+bU5DOy+Ol4G6O1eqhdF+hD4Z7rOi/WxuMcjtiBk7bhiZ5ajEmfqfW7yh15fdIh90jD0XU9BM5943c9ERzriZuHb0f2aJ/erO7pd44MG1n/YqIefGaT6wdgn6cOgRB4XgSyJjyv8U60bxLIIfdNHs/ujgVw+rnCswNRAnZT2PtG1Lfi09voovawS+ysHWJ50/uQ3+Me5mgUhUAIXAWBrAlXMQxx4gkI5JD7BNCvwSQHM/5jBf4seXThcFj/HLHneu3QdrR/e/T5Ntc/DS314ScZ/EyBQyUbySUKvnCw5Xfb/BmSTy98UeFwbqHP3kO6fVKHQAjcDoGsCbczlonkfAI55J7PLj1vnABfADi8Lr2h5VDJ77H9/dulcHDQ5icIfKbfBGO3vt099TfIl/I7ekMgBJ6OQNaEp2Mfy9dDIIfc6xmLeBICZxHwrTz/Lq6H4fwE5SyU6RQCN0Ega8JNDGOCOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKEAiBEAiBEAiBEAiB6yKQQ+51jUe8CYEQCIEQCIEQCIEQOIBADrkHQIyKdQK//PLL3YsXL+7eeeedVx/uaf/zzz/v/vWvf93X61ryNARCIARCIARCIAT2E8ghdz+rSJ5B4Keffrp777337j7++OP7Q60qvvvuu/t2Dr4vX760OXUIhEAIhEAIhEAIHEIgh9xDMEbJEoEvvvji/oDLG9tePAB///33/VHuQyAEQiAEQiAEQuBBBHLIfRC+dF4j8Ntvv929//77i4dcDr78bAG5lBAIgRAIgRAIgRA4ksBJh9w//vjj7t133737+uuvj/ThUXT9/PPPd59//vn9b0KJ4auvvnoUu9dk5JNPPrnj81jFQy4/V+CtbS+P/XvcS+Yv+fXll1/ezw/j/PXXX+/nygcffHD3448/2nzxmjj5OQhjfem5ii3j5qcnzDHabqlcMm9uidNRsTBXyKXHnDPn+I5/5P4l1tQffvjhXjcc1sq07qzJP9Uz16QPP/zwImsSvFh7jhoL1k3OCU+9ljG+nFXw5VbKY+fs+gxqVK9psWcT31s4bJAkwCUGkobFg/sjyim+HGHvXB0sAEctAnt94OcKsOaNLv+h2ZGFhQjdWx9tXjJ/ySW/RGnPQy7+XXrDrjlInCz62L30IZdN65tvvrkPmZp55r0czq1rTOfqOKLfJfPmCP/WdFwLwzUf+7O36ZBL/l9iTWXtQDdzeK3Aao/cmo7HeHbpNQkOvEw4Zyxg3dfnpzzk1jk77SuXGK9q81T9p/Z9rJiMY30GKXWFNQm9t3gY2it/ihwDfOmDxCn+XJssb3M/+uij+8V66Y3uQ3xm8XSR7wsV94/5DXjKM3x4jEPuNB8ufci9ZGyZVw+ZFf+vL5v3OZv+wy0/Hw3wvRRj9G4dciHtS5u3gfol16Rzx4K38X3veCqW05yd9pUj/Zts7tV/bt9Lx1T9fysPuSTlnslvoJcCyjcSDlHoT1km0A+6R/+HZm4G00J11FvF5eheP5ny7JIHQS0vzYdLbijYvlRsmVeO7Pm1X/4udQA737Pb6nnuwWoPBde1Ldlp3dnq81TPL7kmnTMWfJnGp2nveGxGS3P2kuO7ZHNP7A/pe8mYuu+7D7kEREKQSDhIoY1DhL855E+kXJM09TevbFr+roQ+/lkXWfpQDLpOAjfR2ua3Vtr83CsY/qf2V5bagi8eEGjvvyesr9V5zhtD2ih8g+GAW/W6KNHGtUUZ2zo3Y6oHMniov9pFJ/21hQzXW5MUzsSqD+ihj22VBeNinMbw0Jrf3/LPiMniyIOuLNYYEN+5+Uvsa7kgG3PYe2pzUN+M31pZ+9LO9d5i7qiP2qIufJdRHX/l6rxl7OG0Vao9r42x5hLPHmte4XMdY66ZH9i3rM0rZWq9ljfMS/RVfowHBRauhTBHD4V1o66ZyOMjn2nclYUj+rrMUrz+dcOxcdy38rjq63ljDPeBDHOCtaTLnMq789EWes3hPWse8rJjLJwnrrGMWWVUc7T3ncZXv/BJtrTpo9yVqyzx3zzxObX+0hffzJ8qM13DmD6VEX2dj9T6Q73kr/KTDdoqsylf8UOfl2LEfs/hnpPmUfW7+mwstQ3/uO9t1Wf61XHmmbqssYk/zktZ0L53r2QckFVnrWlfKjUf6WMsji/9uIYtH/yvpdutsVa5er1kExli9jn2HBf7+8z49LePJ3K01VJjqu2XuH69I25oZ/ExKUxS2nQWAFwDBrgEbmC0AYA2kgc9bkC0oYeCPPfqX2rT5obLrx5P8iQE8F308JGBdKDozIQ1UfCRe/rU0v1FL3JVT2/r3OCBvHHD0oRCFpv4hh4KPrlI+hz/lwr9eN79sg392MMPxxgbR5d60D3ypwvmVmVALsmTOOBkbLb3caAdHT1/6b8nF+hPPtSCPtr0DZvoos3xVJ4xdVxt21NPdumHDWJhrlGM33vauGb88Qt/yIPq733Hhf/psSGGDvQ9xbzCtjESNz7A07m4Na+mMJfyBt1wQrdjaxt2avzIeU9e4lMdG3yGGW11bJBzbPCt6pf1Wrz4ZuzGtpbHnR9xEJv5ZQzoIg7WJNv0gz6Wc3g7b2EhV/Qxns4NxgQu9bk2rZHRb/zAP1jQxjP0c01BD/dVf+3LNTLTutAZI4dv2LMQE9z113HUPnK0wbPL4NdW0Vf8xy45hC4+xEpBL7rwrZfub3/OvTr1D1voM1+pufe5PlUO6EGmxr2VR+Qk7GoeT23o7mOxNc70kYt+2+ZYc0/hOX7AynwiNuJBthZk+OAnH66RI9at0mNAXpbkCDGhEzn8sWjH+Yi/jH/lpmyvJ5vEVvNRfcaljqkvfsEEn/BXbvahNqbadqnr7RnULPckJXjaajLbRm0xKAK3KFcXxq4f2d6mLvVs1ZM8yVDtosOJazIyyCZNfV7tdd94Ng18bzN2Fwl1uiB6T+1k0hd01X4kUmVd+9br7gPPaCNxa5nk6vOtaw6z33777Sjmv7gAt6P+TyDwF339w7j30sfLcdjK3z25MOWZ+uv42FbHED9ZDOr86L4v3U92kSVWN2779viJi/yxYB+ZvnD7vNbGUWN76nmFf/jf5/aeeVVj69edm7H3HOty+kOOWuxbcw7ujIUbF2OCripDfzdf1yj193hpn+bxnjzG7lbe4Ee3iW7z5iG85VPziljqfNmz5qmn9oMLvhFjZcgauDVG6ut+2Y+xglufwzDpPmAPXhTHepLBz60yzX99rWOkXJ3vXPf1f7KHr9U/GbonURsP/V1HtubHVh6ha8rjPW36uGec65hiU1aVBTY7q+6H3Csr2zqLqtvrrq/6UvOq+7dn3dVGryebrEN9DcAG+eiYo2fqSx5UGfT0PO7+d5+OvN+eQc0aztbBcgBrkthW5ZaCAgigLF0/7b1tSZc6ej3JYxO906cmKLqIh8VC2aq/+8azaeB7m4wqN/rqq7Zq7YJFAsEN2Zr41a/puvuw5it2zy0cZP/5z38ududwi37+5YUjimNZWTKG8OkFu7V9Ggfbqpx6eLaUC8h3buqirgWfPdDQrt4qs/d6skvfHqttLl76hlz/VN+W/LB/jc2x6Pq4f4x5ZYx97GQ0+eW8Wopz0mns3Q76pza4WOxbufGMDZ/+FP2tBxLaOUh1G/3+XsHCOuQzbC/l8aSPthrDJKNuav1Hrn+2eE98zlnzJj3VR67JSXO2xjf1ta2OL3340LYUV4+/3jO+xEZbH2v96j73e1n3duZwjYm9gn2j+knfPi+7HuOm3irYIB5sE1NlRd/e1u8n/TKuz/a22eeUcabPxHTJJjFYPFjXuOW3xRkdk43JF9vc/+mHH9Nny263ueQvttCPvKX3tZ0aPUtrjP5X+Utdvx6dnRYIchrAOgGEVOWWguqQun7c6m1LupZCmOSxW/2b+rLoIMe3NxJl0tN9Q0+PaWqTUeWGHDbov1Xoh5yH3S15nu/xSzniOrfwe1t+f8sb3al4yD36Te7Estvv4zWNg201P/bkwpQf6uq+2e4CxNjUNw7d77X7yS7yPVbbzC99WNO99sz+NTZ0V25T/z0sJ9/35u/UF5+Me/Jpq63rNPYea5dDL23Vtn0rN+TqOHrdc8K+1e5kE30Tr3PZ9xiWbMoR/2rMtu+pjbHz4R6de9e8JT34wDMOYnyxUG/1d+prW2VPH/YHv6D08cIWrOi7VBzr/hzd9N0qa/1rTOhRljzg0HLul9nJJ7+I8CUa/VOO9LZ+P+klhh7H3ja4nzrOlVP1Z8lmHyMOdtiEAYV7ctYDadXZrycbjlmVtc28oh9t55Ru0zxnPHshVuQtvS/te9YY/VfPJevtGdSs96QUiLARt61CXwqKBaJC6/rR19uWdDVXX91O8tjsf3qgA4mI/9TTt158qaX7xrNp4HubjCo3+urrNCH6ny6Rpw0/fTtXfevX3QeeL7X1OLuutXsPsdN/XObvci/9m9wl//p4TeNgG2NB2ZsLjl21ra4+zsjA3sVwysWqZ+16sot8j9U27FL0bSmv7oVW/sf+NTZ0T7E81rzC3SluGe2dVz3srtPYzRHlu5z+yJx7+1ZutNc/6+lvHxv71g1osok+bFa7e/N40kdb1cU9B7te/NKm/+fwNsbOR1t717wlPbbr68RKmeqDbXXMZUyc5D1rcT/owmpanx1bWfV+6KbvVrF/l8OfPkY1B4h/8qvrMe6ac8rIEB967PheWdGnt3HffUROvVzLWJt72/R7TZcy1LVMTJf86GMEY2JibecZ/frYVlv1erIx+WKbftNvbd2tNvp1tymTKTf6ePW+Nb+0o6/eU09t9fmR19szqFkjSBy0CETYtNtW5aag/LZXkxD9FS7Qus1Jl/5MNfrQUQsTlrZqm+fIYtMYalyT3e4bOqaEIwFpt0z6ecZkQGf9kxLtLIh1UVQP9eRXfe51T0jal9o6L3Xsqf0/gOAgW/n+/vvvd5999tl9fEe9xTUG/K1jteRnH69pHGyDK8X7qn9iPrVNffXNZyyGlZPP99aTXfr2WG2recjGRG6S8xbmpbHbNtXkY+f+1PMKP6e498yrKUbbuk7HrnPqcvpTmduXuhbywHmvv7UfsjL3LZH6ux+097k92Z1yZ08M6EauxkAOuXbrv/EYZ13HbOv1kp9VbvK7Pud60kP71HcPK/VV1rUf8U/ziQNPPwDih2yY+xNzGfe4+v0Uz7S32k95v2DbvlQbFzHUdYIx9uBbOahniqm3GeNSHqELmX6A6/upcshajNP7ScYxrfaR29NXfcRUCz50ffX52jV9awzITr7Ypp2tdfdUm4x1H2/GnljrXtX9nXjqa/VhaqvPj7x+c3Q2NLtw1W9ewsVpiwuxCx7tBsXEBhYf9PTkZeIBFx3AtF9NHG0CFJ/qgq8P1thBJ4ODrKW2oxs71sgYqwsRtniOnrpQ4yvtFNop9EEOP2mDA3Eiix5sG0Nled+5/DYPv/ELfdpARj/Rwwcd9bl6am286OSaYltPZnzFf+Wqnq1rfo/773//+/6nCvw/nL148eKOwy76+Hz66ae7/p/PiKf7NdnGR/2tk2+SdUwrc8cBzhbGDF/NX/tt5QJ66VfzTP3UUzHO/gwfam715/VeG3U+6HPNC1jhX51z9sUW8fKpOVLt9GvjrbGZU9jB9mPPK+OeYtDfpXnV4/NenTVvPJiYI8hOcpU51xTGyXFw7YITY+A9cnUd4Z5njN1kcyle5xD5pH9reazMVt4Yg3mD//hGf8u5vM3JmlfmEgz5oLv6qM1aq6eOG89td72ghh/+8wzOyqytC/hBH8bSsXXtoN2xlClysEcn9mBoUQ92KfRBhj7IqV/5WqMPOfoiZ544zlWWa2QYt86ly9V7beAT1+Qg/PULXejEb9rkZ7zokkO1uyeP0GF88MU2vLAnG2ziGx990oe1cdYnYqKfY4KP2OQ5Rf3YVD/tjlttox9+wIePDNR9r3Dhf+SIPmKl6EvtDwPsKKN/tGGTeKwXTL1qnmy6vuE7uvlwTby19L7yRJaCz/ihr/prTPKtOo++3n3IxVkc9SNA76mX2nDaSULw9hFgDQo7JBIyLubcc+2iAXCT3ASuOrzuPuujz9EnbGzgYy0mEgmLLgao+oWsyUDs+EVxkdEe7TwnXvTIgufKVLvIY1sOnRP3+GR/YtB21VOvlbXmmdfWEy/anqIQU12wJh86R+OYZHtsS7nadSJH2coF5LRPjZ4lXdU/FmJ092KumXv9eb1n7Ot86LHiz9SmDnwwn4hjz8JTY/VafU81r9ZixLeteaX/te469+bNGnN1kuPO8SXu5JAyjBFjZVGP/KlrYRzpSz/HdC2PJ31TmzbIUfOG/NOGz8/h3ecM95RT17yuB74W/OIeXtTkq+st6/nUd2qr3LmGlXp9RhuFGka0w8zNvvpEjDxnzLCHLnKky9rHWs6OBXXNE+VqvUdvlecan8xF+mPXwthrH5Y8I17aePaQPNraT/FB3tbY2xpnfZe76/A0huq1nuKhjYI+x1p5a2W03WtY1TnbfTEv1EdNG2Vr3e22vO82ba/zG58cV59TT32Rwy/GnnjRw/0SX/2veo+8fnNVPFJz00UgBJoSAiHwmgCbBYvTUmHesJCk3A4BN8itDe92Ik4k10aAQ1jK8QT8wtE1085afukDXbeb+7u7Rzt15pCbdAuBNwn4zfvN1td3LIy8FUi5LQI55N7WeL5t0fC2eutN79sW07X4y5vXpYMsbzTX/vJ8LTHcmh+Pdsj1tfvaW6tbg5t4QqAT4IDDXPBPWktvaWlnI+Kgm3JbBPzCnw3vtsb1mqNhLWHd4U/GvMXNunKZ0YItf9qHt3+poWau++f6y1iO1iUCj3LI5WcK9bP0TWfJybSHwK0QYMHzN1cugrcSW+LYJuCXfddD7lNC4NIEOHSRc+RbXjRdjjZfHjjM+vtkmecL7eWYb2l+lEPulhN5HgIhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBMhEAIhEAIhEAIhEAJHEsgh90ia0RUCIRACIRACIRACIXAVBHLIvYphiBPPgcAXX3xx9/Lly+cQamIMgRAIgRAIgScnkEPuQUPwxx9/3H333Xd3H3744d3XX399kNbno+aTTz6543OJ8uuvv9598803dx988MGTjc1vv/1299FHH9399NNPlwjxrdG5NE9++OGHu88///xiOfAQQD///PPdV199dffuu+/e/fjjj69UMc9pI6aHFvS+8847b+h/qM70D4EQCIHnTuDkQy6bEQc5FmQW+C+//PJ+kWcTsPCsfzz4uZj35/btNQeUtcMPersu7+mHv49R2OiwhW1jfQy7t2Jj6ZDLF4eHFnLoqcfm+++/v/v444/v/vzzz4eG81b3X5onrAt8CVmb608VOPnD+sbcPuKQi76qh7hcF3v7U8UcuyEQAiFwCwROOuR6UOCtmIVDCIfdvjnRzqbAgXh608Fbmz0HQg7RyLExLBX0e/B2k6C27YiD0pLt3r4npt4n9zMBxu2oLwweIo7SN3u83JqfKrzJZponS1903uz5NHd+mXZ9eYgXrGlH6HmID+kbAiEQAs+BwEmH3KVNiD/n9UOuh4reLtQ9mwYHWw7QbIhsDGsFO8jVzUMf0PFYZdq8H8v2Ldkhpxi3ow6l5sJR+vaw/v333+/+/ve/3/31r3+9e++99+7rFy9ePPu3ubCb5snS+rKH9aVl9qxXe3zwy39dp/b0i0wIhEAIhMDpBE4+5PInxenNbD88eKh4yCGXPxGyKXiAXXubq0zfPNhM++G3YvK5tc/UR7uFGImfNg5g9ScayvBMFlWHfrlZImebfXnmoZ630Bz0LDBXHzJc9/7KUsPN3zj6JQSb9EMXLNVHTNUW/ffEypt9eaC7ftDv2KFr8kF/0cOXGPyh1C836tRXY7BvfW6bOvxrgbyQxZdaagzEgs9HFH57+5e//OX+N7j+VIFDLz9Z4K3uQwtjSP7VfCEWC3HKpsZNztheWXBddZkP2PH3zPT1z/b1rznatEaXeYHOrXliP8bYHLCt1vhSc4pr7ZA/PKeQP7KhDZ344XyhDXk5kCf21R6y/iUIG16rAz7asI91jZ++jgu1Nq3Rh7+VsXqoqx/EUOPkeeWxNsfkgF30wMRYqr1ch0AIhMAtEXh9gtsRFYuii+TWYUDZpU2LjQBdSwstizIbC0VdLPBLBTtdH5sHbXyWChsDiz4yfaOrB03irfr13w1M/cjwzMLmVfvRPrURmxsYfmMbv/SJjdjDgs/X2MmMTdaxIlZ8gRU+ohtdyKDfsidWN2x120c96FaGNmLDJ7l5SEKOdnzoudJZItvl1to6z64Pn+EMA/Qg38dKJqfU9YBLv/pTBa7ff//9O/5DtIcU/Da+6rvjgW7jMW+0x7g4Tsqpy9wy97h3zJChr/mjvlqbB+amfelXSx8LnqG350Dtgy/q1wfsEAv68I9CntuGfWzDixpWXJt/9PfQpy3nSZepuVHt2o8aH2pO4Sf98J1Cv6qHNuzJiecW/MU326i5Rz9x8EEGfWtzDH08Nw8cY/VqL3UIhEAI3BqB5dPfQqQsyCyyLKz1ANXFWUCR2fosLbQs+nXD7ptFt+dzFn2KCzn2Xdx7H+/dYNyI7O+myT0bHhuMhQ0G3fStpbfJocbZ22AKy1rcvNxoia/ywNeqs/b1Gl/oVwv3Uxuylj2xoqP7TFvVgz7uO3/aOrfJr71yvS/j1n2TZ7XLeNYxd0w5EJxb+A/L6tva/q8qHHHI9aCHvxauibnnKPc1j5Hn3rj35J75WvNPu73ekzv0IJ72wwAACUtJREFU2Tu2Xb99e06Ze8Y1zWn64l/ngS78gQUFjksydc5p477T/x1W0aMP2qu6ZVn1IDe140ePE//RR23hvsvRVnMdPnX88LH7oL7UIRACIXArBF6fbE6MiAWTRZjFlNoNQjUu2iyuU3GDmBZaNmwO0rWory/myrjJeQDXr7rQK9tr7PXDAP16TPRDlg3G2Lv+vrnod42zt8mCvv3jZotNfEQWH/YUdHX+3E9tyPayFqtvymof9MKlFvROjCYfetvUF5ku19v6WOKPzPXFe2z0T4+hxrN1zU8T+P2t/1QY9/48oR+At3QtPSfeyUcPazXXzC0PXoypOYV+n3cG3Csnq6p3yTfb13IHGfQ7Fvbp42h7r6e+Hv700bh6X2xMsdLGmsac97r2VZ/6eWabcv3e9lovsezt3teDKXrgin/EYeEe27V0mXPWj6ov1yEQAiHwNhL4/082J0bBIsyhgo8bKSpcpOtiXFW7ISDXi89YqPsHOyz0vbh5Tfq67HTPho4tYkD/9DbPjYLDBHLIT5tLbZND9au3Ib/EqfpKP+RgUG1UmXqNf10v91MbsrVsxcphoPrR79W1xGjyobdNfZf8r32nfjKXm/f6eVTd39Ryz0GXwsGXA7D359okVtj3QmzETmwW89QDKzL1OfeVnf1qLavarz7v11u5g/w0RtPYdt1LfbuPsuj9scGzpdL1KKe+ysC2LuP9VC/p7+3ew7IX2NUxm1h2GXSgk3513nbduQ+BEAiBWyLw5slmI7Klt6jTn4JdpOtiXNW7QSBXCwdM3lJNB1n7UPeCHRb2rq/LLd17GCBGDu79DQo22Rzq292lzaX6J4fqV28zrilmf35R/aYNX5bGQ9lpo4NTHxPZ2W9vrGzAvtFd2jiXGE0+9LapLzJdrrfRr39Jkblj4/0SX1mcUvPTBH5v65tb7v/2t7+9+v0t7Uf8W7nES4w9X8yjmqP47xc45Ds7+3Rd9JONrKi3yt7c2Tu2k72pL77Sro/G1fsTf/8rETLET19j7YdL9amfPrZpw/vOn+euJ+qvenje272f5jhx1vyeeNDWx1o/964fyqcOgRAIgbeVwEmHXBbNaQH3gFg3BhfppYXWDaEv9rTzmQobEYcpPn1Txk7d5Kb+W21sHOiefKatty9tLtV/OXhgwIe+IcMUXb5t00/k7Fd18px7+qwVnnefpzhoq7omGZ5XH4ir65586f2Q2evX1Beb/ZDCffWFL0k9RxyHGgMy9K25RC5XmSmmpbZ+yK0/Vaj/MRrX/L+fIX9OYZ5NbDgQTT9jML+I1cOWdn22lnuyo94qe3IHHZP/U9/J3tQX/2vsS/NDdp0D7MgD17KaT/igvsrANn2UU++LTddNZaoe+k/t5GfPY3wk/ur/xIO26kfP6e67MaQOgRAIgVsisH5KapGyaLLossB6MGBTYPPsizEyLLRLb2V9A1gXXxZ69TfTr26xhV42NX2gtr0u/q867bxwo6k+2dUDMJsV9tws8UN5Dwz1LYubJv6hH1k2VGKgdvOTB7yQQW/dpLimHdt8kK/P9dO62rWNfujvYyI7+lD2xIovbqT4oX9wwQ5FHtVPntEPm5Ylv8gF+3rYhwv9sUMbDNGFLHzRRbs2jEnm9Je5Y0hfnvPpbPRxb11/ruBPFTjU8m/lUh9RiLHGjE7njpy6HTgSJ3172co9OSG3VfbkjnlR9S3lwGSPsXW8ee68rbEbE77Xoh10mLPWytVcQZ4PMuadOaUN84n+yqmTfONjMXbnMr5TZFz9dQ2lv35wXeeO+rBnQdb8t01/1IPv9qENnt7bJ3UIhEAIvO0ETjrksvizILJAcxhws2HhdeEHCO39Qx+KG1J/3tuVr4B7H+6Rm9prv1OuWeiJsRc2E2OWA5sNbTzr/uOTxQ2MjYSNC1n60a4tavQiQ183NnVwr32es0nZVxnryZdT2rZixY4y1SfHAYZ77aHLftbG4SZfx4Q8gzuytlPDB5sWDjz6xnNYc0++1Fy1XX3E9ZDCf1zG/+FD/z+A+OWXX16prQfhV40nXjD2xCwzmNT4uzp4THMKubXc6/MLlmvFvMCvU+aJcVivxYIMfji+1PWAyzP1UPe4GX8PqMy3/pz4aKtzkVhgTE7Cq9vQ3zou9KdfL46bzzrj6k/NY/Vhg4LNGifXUxuya+sH+mAIk5QQCIEQuCUCr09itxRVYrk4AQ/r3ZCHnN7+3O5fvnz56re5PXbe6PL/hJZyHoHp4HqepvQKgRAIgRC4ZQI55N7y6F4oNt8WLal/7m+E/KfClv4VBQ7AS8+WmKb9NYEccl+zyFUIhEAIhMAygRxyl9nkyQIB/oTKQYPDLNf8+ZcPh1/+LPrQP/kvmH1rmtf+wzIOwP/zP/9z2O9z3xooBznqFyzyLCUEQiAEQiAE1gjkkLtGJ88WCXC49XeNHHj5TR8Hj/p718XON/5g66cKHHI57KacRqD/dpW8SwmBEAiBEAiBJQLZJZbIpD0ELkCAAzD/Vm79D9EuYCYqQyAEQiAEQuDZE8gh99mnQACEQAiEQAiEQAiEwO0RyCH39sY0EYVACIRACIRACITAsyeQQ+6zT4EACIEQCIEQCIEQCIHbI5BD7u2NaSIKgRAIgRAIgRAIgWdPIIfcZ58CARACIRACIRACIRACt0cgh9zbG9NEFAIhEAIhEAIhEALPnkAOuc8+BQIgBEIgBEIgBEIgBG6PQA65tzemiSgEQiAEQiAEQiAEnj2BHHKffQoEQAiEQAiEQAiEQAjcHoEccm9vTBNRCIRACIRACIRACDx7AjnkPvsUCIAQCIEQCIEQCIEQuD0COeTe3pgmohAIgRAIgRAIgRB49gRyyH32KRAAIRACIRACIRACIXB7BHLIvb0xTUQhEAIhEAIhEAIh8OwJ5JD77FMgAEIgBEIgBEIgBELg9gjkkHt7Y5qIQiAEQiAEQiAEQuDZE8gh99mnQACEQAiEQAiEQAiEwO0RyCH39sY0EYVACIRACIRACITAsyeQQ+6zT4EACIEQCIEQCIEQCIHbI5BD7u2NaSIKgRAIgRAIgRAIgWdPIIfcZ58CARACIRACIRACIRACt0cgh9zbG9NEFAIhEAIhEAIhEALPnkAOuc8+BQIgBEIgBEIgBEIgBG6PQA65tzemiSgEQiAEQiAEQiAEnj2B/wX/ijEbpafYIgAAAABJRU5ErkJggg==\" alt=\"\" width=\"697\" height=\"502\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLouvain algorithm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Louvain algorithm utilizes multi-level modularity optimization to extract the community structure of large networks, and consists of two iteratively repeated phases. In the first phase, the algorithm computes a partition of the given network through modularity optimization. This phase stops when the modularity reaches a local maximum, indicating that no individual movement can improve it. In the second phase, the same community is folded to create a new weighted network. The first phase is then reapplied to the resulting weighted network and iterated. The algorithm aims to maximize modularity, defined as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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alt=\"\" width=\"720\" height=\"250\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e41 China Unicom. Number of China Unicom mobile subscriptions from 2012 to 2022 (in millions). Statista https://www.statista.com/statistics/233968/number-of-china-unicom-mobile-subscriptions/ (2023)\u003c/p\u003e\n\u003cp\u003e42 Dou, X.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Near-real-time global gridded daily CO2 emissions. \u003cem\u003eThe Innovation\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e3\u003c/strong\u003e, 100182 (2022).\u003c/p\u003e\n\u003cp\u003e43 Tibshirani, R. Regression shrinkage and selection via the lasso. \u003cem\u003eJournal of the Royal Statistical Society: Series B (Methodological)\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e58\u003c/strong\u003e, 267-288 (1996).\u003c/p\u003e\n\u003cp\u003e44 Hoerl, A. E. \u0026amp; Kennard, R. W. Ridge Regression: Applications to Nonorthogonal Problems. \u003cem\u003eTechnometrics\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 69-82 (1970).\u003c/p\u003e\n\u003cp\u003e45 Abeywickrama, T., Cheema, M. A. \u0026amp; Taniar, D. k-nearest neighbors on road networks: a journey in experimentation and in-memory implementation. \u003cem\u003eProc. VLDB Endow.\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 492-503 (2016).\u003c/p\u003e\n\u003cp\u003e46 Cortes, C. \u0026amp; Vapnik, V. Support-vector networks. \u003cem\u003eMachine Learning\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e20\u003c/strong\u003e, 273-297 (1995).\u003c/p\u003e\n\u003cp\u003e47 Breiman, L. Random Forests. \u003cem\u003eMachine Learning\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e45\u003c/strong\u003e, 5-32 (2001).\u003c/p\u003e\n\u003cp\u003e48 Chen, T. \u0026amp; Guestrin, C. \u003cem\u003eXGBoost: A Scalable Tree Boosting System\u003c/em\u003e. In \u003cem\u003eProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u0026nbsp;\u003c/em\u003e785-794 (ACM, 2016).\u003c/p\u003e\n\u003cp\u003e49 Ke, G.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e\u003cem\u003eLightGBM: a highly efficient gradient boosting decision tree\u003c/em\u003e. In \u003cem\u003eAdvances in neural information processing systems (NIPS)\u0026nbsp;\u003c/em\u003e3149-3157 (Curran Associates Inc., 2017).\u003c/p\u003e\n\u003cp\u003e50 Lundberg, S. M. \u0026amp; Lee, S.-I. \u003cem\u003eA unified approach to interpreting model predictions\u003c/em\u003e. In \u003cem\u003eProceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS)\u0026nbsp;\u003c/em\u003e4768-4777 (Curran Associates Inc., 2017).\u003c/p\u003e\n\u003cp\u003e51 Shapley, L. S. A Value for n-Person Games. \u003cem\u003eContributions to the Theory of Games\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e, 307-317 (1953).\u003c/p\u003e\n\u003cp\u003e52 Sundararajan, M. \u0026amp; Najmi, A. \u003cem\u003eThe Many Shapley Values for Model Explanation\u003c/em\u003e. In \u003cem\u003eProceedings of the 37th International Conference on Machine Learning(ICLM)\u0026nbsp;\u003c/em\u003e9269-9278 (PMLR, 2020).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3657266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3657266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate identification, quantification, and continual monitoring of carbon emissions constitute pivotal elements for proactive climate interventions. Conventional methodologies like direct measurement, including point-source assessments or remote sensing, often face challenges related to high costs or limited accuracy. Especially in low- and middle-income nations experiencing escalating emissions, carbon monitoring heavily relies on the administrative capabilities of local governments, which frequently lack adequate monitoring infrastructures. Addressing this predicament, our study introduces a computational framework to forecast CO\u003csub\u003e2\u003c/sub\u003e emissions by leveraging comprehensive observable human activity data from third-party sources. Our findings elucidate a robust correlation between multi-origin CO\u003csub\u003e2\u003c/sub\u003e emissions and human mobility (\u003cem\u003er \u003c/em\u003e= 0.89). Notably, machine learning models adeptly predict these emissions by integrating characteristics extracted from temporally aggregated, anonymized mobility networks (R\u003csup\u003e2\u003c/sup\u003e≈1.0). We demonstrate that the model effectively captures the notable reduction in CO\u003csub\u003e2\u003c/sub\u003e emissions during the COVID-19 lockdown, with both human mobility and CO\u003csub\u003e2\u003c/sub\u003e emissions in China decreasing by 56.97% and 32.45%, respectively. The prediction accuracy remains high for countries with varying social economic development, such as the U.S., Italy and Mexico. This study presents an inexpensive, real-time, and robust method of quantifying CO\u003csub\u003e2\u003c/sub\u003e emissions on a large scale with high precision, and it could facilitate tailored CO\u003csub\u003e2\u003c/sub\u003e emission reduction strategies, grounded in robust scientific evidence derived from the dynamics of human mobility.\u003c/p\u003e","manuscriptTitle":"Inferring Carbon Emissions from Human Mobility Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-05 18:35:50","doi":"10.21203/rs.3.rs-3657266/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"302f8030-624c-4172-a1c6-0816d172636f","owner":[],"postedDate":"January 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27956158,"name":"Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction"},{"id":27956159,"name":"Scientific community and society/Social sciences/Climate change/Projection and prediction"}],"tags":[],"updatedAt":"2024-09-09T16:42:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-05 18:35:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3657266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3657266","identity":"rs-3657266","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}