VR Visualization of Carbon Emission Supported by Small World AI

Digitized Cleaner Cities Lab, School of Urban Planning & Design, Peking University

Global warming poses a significant threat to human survival, with climate change-induced extreme weather events and rising sea levels affecting billions of people worldwide, both directly and indirectly. In some regions, the concentration of buildings and their intensive use leads to localized emissions of carbon dioxide and other gases, impacting local air quality and the health of residents.

According to research by environmental organizations such as the International Energy Agency (IEA), the construction industry is one of the major sources of global energy consumption and carbon emissions. Urban buildings play a crucial role in global greenhouse gas emissions, making the monitoring of urban construction carbon emissions a key strategy for reducing greenhouse gas emissions and mitigating climate change.

The use of visualization tools for monitoring allows carbon emission data to be presented in an easy-to-understand and intuitive manner, helping people clearly comprehend the specific contributions and impacts of buildings on urban carbon emissions. Additionally, VR technology offers a richer and more immersive experience, enabling a more vivid display of carbon emissions produced during the use of buildings. This visual presentation can further enhance public and policymakers' awareness of carbon emission issues

VR Visualization of Carbon Emission Supported by Small World AI introduces an innovative solution for building carbon emissions by integrating Small World AI with Virtual Reality (VR) technologies.

The essence of this solution lies in utilizing Small World AI to calculate carbon emissions from urban buildings and employing VR technology for intuitive visualization. This makes the data more accessible and comprehensible to the public and policymakers, thereby fostering effective environmental actions.

Small World AI Technology: Small World AI is an advanced data analysis technique that models and predicts connection patterns within complex networks, combined with digital twin technology, to deeply mine multi-source urban data. In our project, this technology is used to precisely calculate the carbon emissions of buildings. It integrates and analyzes multi-dimensional data, including energy consumption and building materials, enhancing both the accuracy and efficiency of calculations.

VR Visualization Technology: The carbon emission assessments are mapped onto a three-dimensional city model using Virtual Reality. This allows users to see the carbon emissions of each building in real-time within a simulated environment, significantly enhancing the intuitiveness and interactivity of the information.
VR Visualization of Carbon Emission Supported by Small World AI is designed to provide an immersive, real-time visualization method of building carbon emissions that is available to everyone wherever and whenever.

1. The public: Our solution allows the public to view a building's carbon emissions anytime, anywhere, and to immersive display these carbon emissions in detail in VR. In this way, we help the public more intuitively feel the seriousness of the problem of building carbon emissions and raise their environmental awareness. Further, increased public awareness of carbon emissions will inspire more environmental action and create broad social participation in climate action.

2. Policy makers: Effective monitoring can inform policies and practices that promote a more sustainable and resilient built environment. Also, the visualization of the carbon emission data help policy makers to better understand the sources and drivers of emissions.

The project team members have long focused on the study of urban carbon emissions and have previously led research projects on the visualization of carbon emissions in Shenzhen, China, and Tokyo, Japan. The research project leader, Mr. Zhang Haoran, has collaborated with the Shenzhen Gas Corporation and possesses extensive experience in researching oil pipeline planning. In the fields of smart logistics systems, urban epidemic and disaster prevention, and big data mining of human mobility, significant scientific research achievements have been made. Received two first-place provincial and ministerial Science and Technology Progress Awards, one second-place award, the Hyogo Governor's Best Paper Award in Japan, the Global Smart 50 Award, and the KDD CUP Global Top 10 Award for several consecutive years. Currently, over 110 SCI-indexed papers have been published, with 70 in top-tier journals of the first quartile; also serves as associate editor and editorial board member for several SCI journals. 

Project leader Mr. Zhang Haoran also leads several technical teams. The first team mainly focuses on human traffic trajectory analysis and prediction, working closely with Professor Ryosuke Shibasaki from the University of Tokyo and Professor Song Xuan, Dean of the School of Artificial Intelligence at Jilin University. This team, comprising over ten members, has collectively analyzed a trajectory dataset of over 100,000 people spanning more than a year in Japan, extracting features for classification. They also collaborated with Professor Qin Yu to develop a system for visualizing and computing urban carbon emissions. The second team is dedicated to autonomous driving logistics, founding the Shenzhen Ant Group Autonomous Driving Co., Ltd. with Muhua Zhao and Wenzhao Liu. This company, which has more than twenty employees, has developed a city-level energy-saving and carbon-reducing smart logistics system, and won first prize at the 2020 Shenzhen Innovation and Entrepreneurship Competition. The third team specializes in optimizing urban gas-electric-cooling systems, developing more efficient systems, and conducting tests in a Shenzhen lab. They successfully improved the joint optimization efficiency of the urban gas-electric-cooling systems by over 10%. Overall, Mr. Zhang’s teams continually focus on energy conservation and emission reduction, implementing numerous relevant projects and accumulating ample experience, qualifying us to lead the development of the current project.

We have successfully developed and deployed a prototype of our Dynamic Urban Simulator in the 23 wards of Tokyo. This initial implementation served as a crucial testbed for refining our system and verifying its effectiveness in accurately simulating human mobility patterns within an urban environment.

Our prototype has not only been tested in Tokyo, but its core technologies have also been adapted and implemented in other major cities such as Beijing and Shanghai. This demonstrates the system's robust adaptability and utility across varying urban contexts and data environments.

Key aspects of our prototype include:

Integration of Diverse Data Sources: We've harnessed multiple types of data, including GPS user data and ticket gate count data, which are instrumental for our simulation's accuracy and applicability in real-world scenarios.

Real-world Applications: Our system's technology has been employed in several impactful projects: In Tokyo, it was used to estimate the scaling factor for railway users, enhancing crowd management strategies at Japan Railway East stations. In Beijing, it helped simulate human movement patterns for crowd evacuation during extreme weather events, aiding in risk assessment and safety planning. In Shanghai, it was applied to analyze electrical charge demand for vehicles, improving the placement strategies for electronic charge stands.

Carbon Emission Visualization: Our system has completed the development of carbon emission visualization. Now it can generate carbon emissions on the map based on the calculated carbon emissions and location pairs, and it can be converted into images and video outputs.

These applications have not only showcased the practical value of our prototype but have also provided us with invaluable feedback for further refinement.

As this project is currently in the prototype stage, we have not yet established a customer base in the traditional sense. However, our prototype has been utilized in several cities and has indirectly benefited a broad audience through its application in public safety and urban planning projects. While direct customer interactions are limited at this stage, the societal impact and indirect beneficiary reach of our project are significant.

Our solution is currently in the prototype stage, may facing barriers across multiple domains that require support. 

In the technical area, our primary objective is to amass a more extensive dataset to enhance the three-dimensional modeling capabilities essential for our solution's effectiveness. Specifically, we are exploring avenues to acquire multi-source and multi-modal data to refine the quality of our Small World AI, with the goal of significantly increasing the precision of our carbon emissions predictions. This endeavor involves not only the aggregation of vast amounts of data but also the integration and analysis of this information to derive actionable insights.

On the commercial area, our efforts are geared towards identifying and engaging with potential partners. This search is not merely about forming alliances but also about co-creating robust business models that can successfully commercialize our Solution. We aim to develop a framework that not only supports the scalability of our technology but also ensures its market viability and sustainability.

Moreover, we are examining strategies to broaden the reach and relevance of our Solution. One such strategy is to integrate our advanced visual assessment system with widely used public platforms, such as social media, to enhance public engagement and awareness regarding the environmental impact of building carbon emissions.

Additionally, we are deeply cognizant of the diverse challenges that may arise when implementing our Solution in different regions. These challenges include navigating the complex landscape of cultural norms and legal regulations that vary significantly across regions. Understanding and addressing these potential obstacles is crucial for the successful global deployment of our technology.

By seeking involvement in the Solve initiative, we aspire to obtain crucial support and guidance to navigate these multifaceted challenges. Our goal is to leverage the collective expertise and resources offered through Solve to optimize our Solution's development trajectory, ensuring its technical efficacy, commercial success, and positive environmental impact.

Our project introduces an innovative solution for building carbon emissions by integrating Small World AI with Virtual Reality (VR) technologies. The essence of this solution lies in utilizing Small World AI to calculate carbon emissions from urban buildings and employing VR technology for intuitive visualization. This makes the data more accessible and comprehensible to the public and policymakers, thereby fostering effective environmental actions.

Innovation Highlights:

1. Small World AI Technology: Small World AI is an advanced data analysis technique that models and predicts connection patterns within complex networks, combined with digital twin technology, to deeply mine multi-source urban data. In our project, this technology is used to precisely calculate the carbon emissions of buildings. It integrates and analyzes multi-dimensional data, including energy consumption and building materials, enhancing both the accuracy and efficiency of calculations.

2. VR Visualization Technology: The carbon emission assessments are mapped onto a three-dimensional city model using Virtual Reality. This allows users to see the carbon emissions of each building in real-time within a simulated environment, significantly enhancing the intuitiveness and interactivity of the information.

Broad Impacts:

1. Enhancing Public Environmental Awareness: The VR display vividly illustrates the specific impacts of carbon emissions, bolstering public awareness and participation in environmental issues.

2. Aiding Policy Formulation: Decision-makers can utilize precise carbon emission data to make scientific decisions, optimize urban planning, and architectural designs, and implement environmental policies.

Market Impact:

1. Driving Green Transformation in the Building Sector: The project could encourage the building industry to adopt low-carbon technologies and materials.

2. Promotion of Technology and Investment: The solution demonstrates the potential application of Small World AI and VR technologies in urban planning and environmental protection, which may attract more investments and foster the development and widespread adoption of related technologies.

Our solution not only enables effective monitoring and management of urban carbon emissions but also ignites societal engagement in climate action, propelling us toward a more sustainable world.

Our solution integrates Small World AI and Virtual Reality (VR) technologies to calculate and visualize building carbon emissions, aiming to enhance public awareness and engagement on carbon emissions issues and to promote the formulation and implementation of environmental policies. Below is our anticipated impact pathway, including the logical links between activities, outputs, outcomes, and long-term impacts, along with evidence supporting these connections.

Activities

1. Data Integration and Analysis: Utilizing Small World AI technology to integrate data on energy consumption and building materials usage of urban buildings to calculate their carbon emissions.

2. Carbon Emission Visualization: Employing VR technology to map carbon emission data onto a three-dimensional city model, facilitating intuitive data presentations.

Outputs

1. Accurate Carbon Emission Data: The advanced data analysis capabilities of Small World AI ensure the accuracy and reliability of carbon emission calculations.

2. Interactive 3D Carbon Emission Model: The public and policymakers can use VR technology to view the carbon emissions of specific buildings within a simulated environment.

Outcomes

1. Enhancing Public Environmental Awareness: The intuitive VR display helps the public better understand carbon emission issues, thus increasing their environmental awareness.

2. Supporting Policy Formulation: Accurate carbon emission data provide a scientific basis for policymakers, assisting them in the development or adjustment of environmental policies.

Long-term Impact

1. Promoting Environmentally Friendly Urban Planning: As policies improve and public awareness increases, urban planning will focus more on environmental concerns, fostering the application of green buildings and low-carbon technologies.

2. Encouraging Social Participation in Climate Action: Enhanced public understanding of carbon emissions will inspire more environmental actions, leading to widespread societal engagement in climate initiatives.

Evidence Support

1. Third-party Research: According to studies by the International Energy Agency (IEA) and other environmental organizations, the construction sector is one of the main sources of energy consumption and carbon emissions globally. These studies support our logic in addressing climate change by reducing building carbon emissions.

2. Evaluation Data: Preliminary user feedback and data analysis indicate that the use of VR technology for visualizing carbon emissions significantly enhances users' understanding of and attention to carbon emission issues.

Through the above activities and outputs, our solution is expected to make significant contributions to enhancing public environmental awareness and facilitating policy formulation, promoting environmentally friendly urban planning and broad societal participation in climate actions

The impact objectives of this plan can be described in the following two aspects:

1. From the perspective of cities and their governance, this plan supports urban planning and policy formulation. By leveraging data and simulations generated by Small World AI, it assists urban planners and policymakers in understanding the factors influencing urban carbon emissions and implementing more effective measures for energy use and infrastructure planning. This use of scientific data supports the creation of environmental policies aimed at reducing the overall carbon footprint of cities.

2. From an individual perspective, the immersive experience provided by augmented reality technology allows the public to visually observe the carbon emissions of buildings and their surrounding environments, thereby enhancing environmental awareness and engagement.

To measure these impact objectives, the plan incorporates indicators aligned with the United Nations Sustainable Development Goals (SDGs) to assess their achievement:

1. Urban Carbon Footprint Assessment: This involves regularly evaluating the carbon emission records of participating cities to compare the accuracy of simulated data with actual data, and refining the model based on these comparisons.

2. Policy Impact Analysis: This tracks the number of urban plans and policies formulated or adjusted with the support of data from this plan and evaluates their outcomes, such as changes in carbon emissions.

3. Public Engagement with VR Application: By collecting statistics on the number of users of the VR application and their feedback, the plan assesses the public’s interest in and satisfaction with the visual representation of carbon emissions provided by the project.

The core of this plan is to calculate household carbon footprints in real time using Small World AI and to visualize carbon emissions through VR technology. Its central technologies are divided into two main parts: carbon emission calculation based on Small World AI and VR technology for carbon emission visualization.

The Small World AI system has implemented a digital twin of the city based on urban big data, which accurately reflects human activity patterns and urban dynamics in the real world. This system features multiple functionalities, including time-series trajectory visualization, building environment aggregation, advertisement visibility area calculation, and accessibility simulation for new railway stations. It also enables a first-person experience in a virtual Tokyo city.

The core of the VR visualization is to create immersive visualizations of building carbon emissions using augmented reality, overcoming the limitations of traditional 2D and 3D visualizations by showcasing data from a first-person perspective using a 3D city model. This method uses scene image segmentation algorithms and registration techniques between real-time video sources and static urban models to ensure accurate data overlay on real-world images. Then it projects carbon emission data onto real-world views to enhance public understanding of and engagement with climate data.

This project operates under a business model that creates value for its key customers and beneficiaries, which primarily include businesses (B2B) and government entities (B2G).

The key customers and beneficiaries including: companies across various industries such as retail, transportation, real estate, and urban planning. Besides, government entities, city planners, public health departments, transportation authorities, and emergency management agencies.

Our financial sustainability plan is designed to ensure that we can continue to pursue our mission without relying solely on grants and donations. Our primary revenue stream comes from offering city dynamics simulation by Small World AI as a subscription-based service. We cater to businesses and government entities, providing tiered pricing models that align with the clients' needs and operational sizes. By entering into long-term service contracts, we offer customized solutions and ongoing support for urban analytics and planning, ensuring a steady income and the opportunity to build strong partnerships. We also recognize the importance of diversifying our funding sources. We actively apply for grants from institutions that support technological innovation and urban development. Furthermore, we seek donations from philanthropic organizations that share our vision for urban sustainability and public health. These efforts not only bolster our financial stability but also strengthen our network of supporters.

To scale our operations and reach a broader market, we have turned to venture capital, angel investors, and crowdfunding platforms. Our recent fundraising round in January 2024 was a testament to our potential, securing Debt Financing worth 1 Billion Yen from three investors. This capital injection is fueling our growth and allowing us to invest in the development of our products and services. Our success is also evidenced by the strategic partnerships we have forged with renowned companies and organizations, including the national research and development corporation and the New Industrial Technology Comprehensive Development Corporation (NEDO). These collaborations bring stability and open doors to new opportunities.

Our financial model is designed to ensure that our operational costs are covered, and any surplus is reinvested into research and development. This approach ensures the continuous improvement and innovation of our Small World Ai and VR visualization  solutions. As we look to the future, we are confident that our sustainable financial model will enable us to make a lasting impact on urban environments globally.