CN: Total Inland Transport Infrastructure Investment data was reported at 5,680,000,000,000.000 RMB in 2022. This records an increase from the previous number of 5,490,000,000,000.000 RMB for 2021. CN: Total Inland Transport Infrastructure Investment data is updated yearly, averaging 1,980,000,000,000.000 RMB from Dec 1995 (Median) to 2022, with 25 observations. The data reached an all-time high of 5,680,000,000,000.000 RMB in 2022 and a record low of 83,492,000,000.000 RMB in 1995. CN: Total Inland Transport Infrastructure Investment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.ITF: Transport Infrastructure, Investment and Maintenance: Non OECD Member: Annual. [STAT_CONC_DEF] Investment expenditure on railways infrastructure: capital expenditure on new railway infrastructure or extension of existing railways, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fittings and installations connected with them signalisation, telecommunications, catenaries, electricity sub-stations, etc.) as opposed to rolling stock. Investment expenditure on road infrastructure: capital expenditure on new road infrastructure or extension of existing roads, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fittings and installations connected with them (signalisation, telecommunications, toll collection installations, etc.) as opposed to road vehicles. Investment expenditure on inland waterways infrastructure: capital expenditure on new inland waterways infrastructure or extension of existing inland waterways, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure) renewal and upgrades or major repairs (repairs improving the original performance or capacity of the infrastructure). Infrastructure includes land, channels and permanent way constructions, buildings, navigation locks, mooring equipment, toll collection installations, as well as immovable fixtures, fittings and installations connected with them (signalisation, telecommunications, etc.) as opposed to IWT vessels. [COVERAGE] Data should include both government and private investment, unless otherwise specified in the country-level metadata. [COVERAGE] Data do not include inland waterways infrastructure expenses since they are not reported.

CN: Total Inland Transport Infrastructure Investment: %: Road Infrastructure data was reported at 87.345 % in 2022. This records an increase from the previous number of 87.139 % for 2021. CN: Total Inland Transport Infrastructure Investment: %: Road Infrastructure data is updated yearly, averaging 75.396 % from Dec 1995 (Median) to 2022, with 25 observations. The data reached an all-time high of 87.345 % in 2022 and a record low of 44.553 % in 1995. CN: Total Inland Transport Infrastructure Investment: %: Road Infrastructure data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.ITF: Transport Infrastructure, Investment and Maintenance: Non OECD Member: Annual. [COVERAGE] Investment expenditure on rail, road and inland waterways infrastructure: capital expenditure on new infrastructure or extension of existing infrastructure, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fitting and installations connected with them (signalisation, telecommunications, catenaries, electricity sub-stations, toll collection installations, navigation locks, mooring equipment, etc.) as opposed to rolling stock or road vehicles or inland waterways vessels. [COVERAGE] TOTAL INLAND INFRASTRUCTURE INVESTMENT Data do not include inland waterways infrastructure expenses since they are not reported.

China Total Inland Transport Infrastructure Investment: %: Rail Infrastructure data was reported at 12.655 % in 2022. This records a decrease from the previous number of 12.861 % for 2021. China Total Inland Transport Infrastructure Investment: %: Rail Infrastructure data is updated yearly, averaging 24.604 % from Dec 1995 (Median) to 2022, with 25 observations. The data reached an all-time high of 55.447 % in 1995 and a record low of 12.655 % in 2022. China Total Inland Transport Infrastructure Investment: %: Rail Infrastructure data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.ITF: Transport Infrastructure, Investment and Maintenance: Non OECD Member: Annual. [COVERAGE] Investment expenditure on rail, road and inland waterways infrastructure: capital expenditure on new infrastructure or extension of existing infrastructure, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fitting and installations connected with them (signalisation, telecommunications, catenaries, electricity sub-stations, toll collection installations, navigation locks, mooring equipment, etc.) as opposed to rolling stock or road vehicles or inland waterways vessels. [COVERAGE] TOTAL INLAND INFRASTRUCTURE INVESTMENT Data do not include inland waterways infrastructure expenses since they are not reported.

This dataset contains four files: (1) Raw data of 13 indicators closely related to society, economy, environment, infrastructure, and innovation at the provincial level in China from 1990 to 2021, including GDP per capita, disposable income per capita, rate of high school graduates and above, density of physicians per 10,000 people, unemployment rate, living space per capita, PM2.5 concentration, carbon emission per capita, urban green space per capita, road density, internet penetration rate, patents granted per capita, and R&D expenditure per capita; (2) Population-weighted coefficient of variation for the 13 indicators; (3) Gini coefficient for the 13 indicators; (4) Moran's I for the 13 indicators.

Fixed Asset Investment in China decreased to 1.60 percent in July from 2.80 percent in June of 2025. This dataset provides - China Fixed Asset Investment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

China Road Infrastructure Investment data was reported at 4,957,710,000,000.000 RMB in 2022. This records an increase from the previous number of 4,780,820,000,000.000 RMB for 2021. China Road Infrastructure Investment data is updated yearly, averaging 1,276,450,000,000.000 RMB from Dec 1995 (Median) to 2022, with 25 observations. The data reached an all-time high of 4,957,710,000,000.000 RMB in 2022 and a record low of 37,198,000,000.000 RMB in 1995. China Road Infrastructure Investment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.ITF: Transport Infrastructure, Investment and Maintenance: Non OECD Member: Annual. [STAT_CONC_DEF] Capital expenditure on new road infrastructure or extension of existing roads, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fittings and installations connected with them (signalisation, telecommunications, toll collection installations, etc.) as opposed to road vehicles. [COVERAGE] Data should include both government and private investment, unless otherwise specified.

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

• coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)
• extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)
• tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

• population (Schiavina et al, 2023)
• built-up area (Pesaresi et al, 2023)
• roads (OpenStreetMap, 2025)
• railways (OpenStreetMap, 2025)
• power plants (Global Energy Observatory et al, 2018)
• power transmission lines (Arderne et al, 2020)

Contextual information:

• elevation (European Union and ESA, 2021)
• land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)
• administrative boundaries from geoBoundaries (Runfola et al., 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

• snkit helps clean network data
• nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

• Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142
• Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif
• Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3
• Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3
• Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.006f2c9a (Accessed on 09-AUG-2024)
• Copernicus DEM - Global Digital Elevation Model (2021) DOI: 10.5270/ESA-c5d3d65 (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved)
• Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/
• Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
• Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616
• Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
• OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org
• Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
• Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: 10.1371/journal.pone.0231866.
• Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088
• Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
• Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.

Description The geometry and attributes, including descriptions and the status of 8 economic corridors in the Belt and Road Initiative (BRI) up until August 2021. Depiction of the economic corridors is based on the transportation networks, including expressways and railways with their status (e.g., existing or new projects backed by Chinese enterprises) annotated in the Shapefile.The list of economic corridors is listed below. Corridors 1-6 have been officially recognized by the Chinese government. Moreover, this dataset visualizes the economic corridors connecting China, Vietnam, and Africa, subsumed under corridors 7-8.

China-Pakistan-Economic Corridor (CPEC) China-Mongolia-Russia Economic Corridor (CMREC) New Eurasian Land Bridge (NELBEC) China-Central Asia-West Asia Economic Corridor (CCW) Bangladesh-China-India-Myanmar Economic Corridor (BCIM) China-Indochina Peninsula Economic Corridor (CICPEC) China Vietnam Economic Corridor (CVEC) The China-Africa Economic Corridor (CAEC)

For a combined visualization of the economic corridors, see: 9. Combined. An interactive view of this dataset: Link Source Data were collected from multiple public sources. Locations of new expressways and railways were digitized based on images in reference.zip. The existing transportation networks, including expressways and railways, are sourced from Natural Earth, Road version 5.0.0 (Published on 7 December 2021) and Natural Earth, Railroad version 4.0.0 (Published on 15 October 2017). The polygons and boundaries of regions are sourced from Natural Earth, Admin 0 – Countries version 5.1.1 (Published on 12 May 2022). For metadata, such as data description and available methods for geospatial data processing, please read the readme.pdf. Terms of use This dataset features in a collection of geospatial data "Geo-mapping databases for the Belt and Road Initiative". To cite this work, available citation styles can be found here: https://doi.org/10.6084/m9.figshare.c.6076193

The 'financialisation' of Chinese housing, land and infrastructure - the use of financial instruments to convert the built environment into investment opportunities - generates momentum and vitality in the Chinese economy and has led to wealth accumulation. This study explores how the Chinese housing boom has been financed in the absence of a more developed financial system, and to what extent the financial sector has contributed to the overall appreciation of housing and land assets. A large questionnaire survey was conducted in six case cities including Shanghai, Shenzhen, Chengdu, Xi'an, Nanjing and Tianjin.The Chinese financial system has fostered rapid economic growth in recent decades through so-called 'land-based financing' (tudi chaizhen) in housing, land and infrastructure development. The 'financialisation' of Chinese housing, land and infrastructure - the use of financial instruments to convert the built environment into investment opportunities - generates momentum and vitality in the Chinese economy and has led to wealth accumulation. Real estate financing instruments such as the real estate investment trust (REITS), mortgage securitisation, reverse mortgages and public-private partnerships (PPP) in infrastructure have been recently invented. On the other hand, traditional real estate financial products such as household mortgages and real estate loans benefit from new internet-based finance. Chinese real estate finance has now entered a phase of 'financial explosion'. However, the concrete channels, complex arrangements and new instruments are not entirely known. This research project aims to investigate how housing, land and infrastructure are actually financed, what are the new financial instruments, to what extent there is a trend of 'financialisation', and what are the risks associated with this transformation. We examine the recent trend of financialisation in terms of the forms and extent of the involvement of both the formal and the unofficial ('shadow banking') sectors in real estate development. Recent developments in REITS and PPP will be examined to show the inflow of financial capital in housing, land, and infrastructure projects. We explore how the Chinese housing boom has been financed in the absence of a more developed financial system, and to what extent the financial sector has contributed to the overall appreciation of housing and land assets. We will also try to understand the potential impacts of financialisation on households, enterprises and local government finances (i.e. the issue of 'local debt') and what are the main factors affecting financial stability. The project investigates three levels of financing mechanisms: projects and enterprises, local governments, and individual households. We choose six case cities: in the coastal region, Shanghai and Shenzhen; the central region: Zhengzhou and Changsha; the western region: Chongqing and Chengdu. At the local government (city) level, we will examine the institutional environment and policies regarding built environment finance, including the involvement of housing provident funds. This research project will assess the recent trend of financialisation in Chinese housing, land and infrastructure sectors and provide a nuanced understanding of the changing financial mode, its dynamics and the new institutional environment. The project will examine emerging financial products and new channels in these sectors and their operational mechanisms. The project will focus on household financial behaviour to understand the new trend of financialisation of real estate and its impact on housing consumption, investment behaviour, and job preference. The project will further assess macroeconomic implications such as the impact on the Chinese financial system, financial product innovation, fiscal policies and company investment. Finally, these findings will lead to an assessment of the potential risks associated with financialisation and recommendations for risk management. The sample was collected through random face to face interview at the site of China Housing Provident Fund Centres in six cities (Shanghai, Shenzhen, Tianjin, Nanjing, Chengdu, Xi’an). Verbal consent was made before interview by the Centre in the same way as other NSFC projects. The rejection rate was 9.6%. The sample reflects the population of housing provident fund applicants rather than the total urban resident population. But because housing provident fund is a mainstream compulsory scheme, the sample reflects the population who qualifies housing provident funds and has the intention to apply for the mortgage.

China Total Inland Transport Infrastructure Investment: Euro data was reported at 801,000,000,000.000 EUR in 2022. This records an increase from the previous number of 719,000,000,000.000 EUR for 2021. China Total Inland Transport Infrastructure Investment: Euro data is updated yearly, averaging 220,000,000,000.000 EUR from Dec 1995 (Median) to 2022, with 25 observations. The data reached an all-time high of 801,000,000,000.000 EUR in 2022 and a record low of 7,647,515,627.000 EUR in 1995. China Total Inland Transport Infrastructure Investment: Euro data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.ITF: Transport Infrastructure, Investment and Maintenance: Non OECD Member: Annual. [STAT_CONC_DEF] Investment expenditure on railways infrastructure: capital expenditure on new railway infrastructure or extension of existing railways, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fittings and installations connected with them signalisation, telecommunications, catenaries, electricity sub-stations, etc.) as opposed to rolling stock. Investment expenditure on road infrastructure: capital expenditure on new road infrastructure or extension of existing roads, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fittings and installations connected with them (signalisation, telecommunications, toll collection installations, etc.) as opposed to road vehicles. Investment expenditure on inland waterways infrastructure: capital expenditure on new inland waterways infrastructure or extension of existing inland waterways, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure) renewal and upgrades or major repairs (repairs improving the original performance or capacity of the infrastructure). Infrastructure includes land, channels and permanent way constructions, buildings, navigation locks, mooring equipment, toll collection installations, as well as immovable fixtures, fittings and installations connected with them (signalisation, telecommunications, etc.) as opposed to IWT vessels. [COVERAGE] Data should include both government and private investment, unless otherwise specified in the country-level metadata. [COVERAGE] Data do not include inland waterways infrastructure expenses since they are not reported.

China Road Infrastructure Investment: Euro data was reported at 699,731,000,000.000 EUR in 2022. This records an increase from the previous number of 626,834,000,000.000 EUR for 2021. China Road Infrastructure Investment: Euro data is updated yearly, averaging 142,354,000,000.000 EUR from Dec 1995 (Median) to 2022, with 25 observations. The data reached an all-time high of 699,731,000,000.000 EUR in 2022 and a record low of 3,407,180,164.000 EUR in 1995. China Road Infrastructure Investment: Euro data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.ITF: Transport Infrastructure, Investment and Maintenance: Non OECD Member: Annual. [STAT_CONC_DEF] Capital expenditure on new road infrastructure or extension of existing roads, including reconstruction, renewal (major substitution work on the existing infrastructure which does not change its overall performance) and upgrades (major modification work improving the original performance or capacity of the infrastructure). Infrastructure includes land, permanent way constructions, buildings, bridges and tunnels, as well as immovable fixtures, fittings and installations connected with them (signalisation, telecommunications, toll collection installations, etc.) as opposed to road vehicles. [COVERAGE] Data should include both government and private investment, unless otherwise specified.

Explore Green Bond Issuances by Country, Sovereign Green Bond Issuances, Cumulative Green Bond Issuances, and more on this dataset webpage.

Green Bond Issuances by Country, Sovereign Green Bond Issuances, Cumulative Green Bond Issuances, Cumulative Green Bond Issuances by Type of Currency, Environment, Climate Change, Financial and Physical and Transition Risk Indicators, Green Bonds, Green Bond Issuances (All Countries), US Dollars, Green Bond Issuances by Type of Issuers, Green Bonds Issuances, Green Bonds, Environment, Climate Change, Financial and Physical and Transition Risk Indicators, Green Bonds, Green Bonds Issuances, All, International Organization, State owned entities, Banks, Nonfinancial corporations, Local and state Government, Other financial corporations, Sovereign, Access to Essential Services, Acquisition, Affordable Basic Infrastructure, Capital expenditure/Financing expenses, Carbon reduction through reforestation and avoided deforestation, E-education programs - Education Projects, Economic Development, Funding new technologies to reduce GHS emissions, General Purpose/Acquisition, Pollution Control, Production/Supply of Cannabis, Sustainable Management of Living Natural Resources, Wind projects, Capital expenditure, Electric & Public Power, General Purpose/Working Capital, Green Construction/Buildings, Merger or Acquisition, Other, Project Finance, Refinance/Financing expenses, Repay Bank Loan or Bridge Financing, China Municipal Development, Employee stock ownership plan, Environmentally Sustainable Products, Equipment Upgrade/Construction, General Purpose, Industrial Development, Infrastructure, Land Preservation, Other Education, Other Public Service, Repay Intercompany Debt, Solar projects, Sustainable Management of Land Use, Sustainable Water or Wastewater management, The Belt and Road Initiative, Acquiring and distribution of vaccine, Alternative Energy, Aquatic Biodiversity Conservation, Clean Transport, Climate Change Adaptation, Environmental Protection Projects, Other Housing, Other Transportation, Pollution Prevention & Control, Redeem Existing Bonds or Securities, Water & Sewer, Working capital, Circular Economy Adapted/Eco-efficient Products, Production Technologies/Processes, Eligible Green Projects, Energy Efficiency, Financing of Subordinated Loan, Gas, General Purpose/Refinance, Property Expendit (acquisit/development), Renewable Energy Projects, Waste Management, Green bond, Sustainable finance



Argentina, Australia, Austria, Bangladesh, Belgium, Brazil, Canada, Chile, China, Colombia, Costa Rica, Denmark, Egypt, Estonia, Fiji, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Kazakhstan, Latvia, Liechtenstein, Lithuania, Luxembourg, Malaysia, Marshall Islands, Mauritius, Mexico, Morocco, Namibia, Netherlands, New Zealand, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Peru, Philippines, Poland, Portugal, Romania, Russia, Serbia, Seychelles, Singapore, Slovenia, South Africa, South Korea, Spain, Sri Lanka, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Arab Emirates, United Kingdom, Vietnam

Follow data.kapsarc.org for timely data to advance energy economics research..Important notesexcluding international organizations type of currency and type of issuers (nonfinancial corporations, other financial corporations, banks, state owned entities, sovereign, state and local governments and international organizations).

This layer shows Major Infrastructure Projects in Hong Kong. It is a set of data made available by the Water Supplies Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of CSDI Portal at https://portal.csdi.gov.hk.

Public Private Partnerships Investment In ICT: Current Price data was reported at 1.669 USD bn in 2022. Public Private Partnerships Investment In ICT: Current Price data is updated yearly, averaging 1.669 USD bn from Dec 2022 (Median) to 2022, with 1 observations. The data reached an all-time high of 1.669 USD bn in 2022 and a record low of 1.669 USD bn in 2022. Public Private Partnerships Investment In ICT: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Investment Statistics. Public Private Partnerships in ICT (current US$) refers to commitments to projects in ICT backbone infrastructure (including land based and submarine cables) that have reached financial closure and directly or indirectly serve the public. Movable assets and small projects are excluded. The types of projects included are management and lease contracts, operations and management contracts with major capital expenditure and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data is presented based on investment year. Data are in current U.S. dollars and available 2015 onwards only.;World Bank, Private Participation in Infrastructure Project Database (http://ppi.worldbank.org).;Sum;

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

The coordinated development of regional logistics and the economy is crucial for regional economic progress and for reducing regional development disparities. This study applies regional coordinated development theory and coupling theory, utilizing the Coupling Coordination Degree Model (CCDM) to analyze data from 31 provinces and cities in China in 2021, with the analysis results serving as the outcome variable. Additionally, we use data from four dimensions: infrastructure investment (II), technological innovation (TI), industrial structure (IS), and human capital (HC), as the conditional variables, conducting a multi-factor configurational analysis using fsQCA. Three paths with high coupling coordination and one path with non-high coupling coordination are identified, and the reasons for each path are analyzed. The results indicate that: 1) there are significant regional disparities in China regarding economic development, logistics development, and the degree of their coupling and coordination, with the eastern regions exhibiting higher levels and the western regions and other remote areas exhibiting lower levels. 2) The three paths with high coupling coordination are: “Infrastructure Investment—Technological Innovation”, “Technological Innovation—Industrial Structure—Human Capital”, and “Infrastructure Investment—Fundamental Innovation—Industrial Structure”. These three types facilitate the well-coordinated progress of regional logistics and the economy. The article concludes by highlighting policy suggestions that underscore the significance of fortifying the bond between the logistics industry and the economy, alongside earnest efforts to enhance regional logistics standards. This will foster a mutually reinforcing and co-developing situation, further promoting coordinated development among regions, achieving high-quality regional development, and reducing the imbalances in logistics and economic development among different regions.

Error-Correcting Code Memory (ECC Memory) Market Outlook

The global market size of Error-Correcting Code (ECC) Memory was valued at approximately USD 12.3 billion in 2023 and is projected to reach around USD 24.7 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 7.8% during the forecast period. The surge in market demand is driven by the increasing need for data integrity and reliability in computing systems, particularly with the exponential rise in big data, cloud computing, and AI applications.

One prominent growth factor in the ECC memory market is the escalating need for data integrity and reliability. As data centers and cloud service providers handle massive amounts of data, even a single bit error can lead to significant data corruption and operational failures. ECC memory mitigates this risk by detecting and correcting data corruption, ensuring data integrity. This reliability is crucial for sectors such as finance and healthcare, where data accuracy is paramount and errors can have severe consequences.

Another driving force is the growing adoption of advanced computing technologies. With the rapid advancements in artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT), the demand for high-performance computing solutions has surged. These technologies require robust memory solutions that can handle large datasets and complex computations without errors. ECC memory, with its error-detection and correction capabilities, is becoming increasingly essential in these high-stakes, data-intensive applications.

The expansion of cloud computing and virtualization technologies also boosts ECC memory demand. Cloud service providers are continually expanding their infrastructure to accommodate growing customer bases and the increasing number of applications moving to the cloud. ECC memory ensures that these cloud environments maintain high levels of performance and reliability, preventing data corruption and minimizing downtime. As businesses increasingly adopt cloud-based solutions, the reliance on ECC memory is expected to grow significantly.

Regionally, North America dominates the ECC memory market due to the presence of major technology companies and data centers. The region's advanced IT infrastructure and early adoption of cutting-edge technologies contribute to its leading position. Furthermore, the Asia Pacific region is witnessing substantial growth, driven by the rapid expansion of data centers and the increasing adoption of cloud computing. Countries like China, India, and Japan are investing heavily in IT infrastructure, further propelling the demand for ECC memory in the region.

Type Analysis

The ECC memory market is segmented based on types such as DDR4, DDR5, and others. DDR4 ECC memory currently holds a significant share of the market due to its widespread use in existing data centers and server applications. DDR4 offers a balance of performance, reliability, and cost-effectiveness, making it a popular choice for organizations looking to ensure data integrity in their computing systems. Its ability to support higher memory capacities and speeds provides an added advantage for businesses handling large datasets.

However, DDR5 ECC memory is emerging as a key segment poised for rapid growth. DDR5 offers substantial improvements over its predecessor, including higher bandwidth, increased capacity, and better power efficiency. These enhancements are crucial for modern computing environments that require advanced performance and scalability. As DDR5 technology becomes more mainstream, its adoption in ECC memory solutions is expected to surge, driven by the need for faster and more reliable memory in high-performance computing applications.

Other types of ECC memory, including custom and specialized solutions, also play a significant role in the market. These niche products cater to specific applications and industries that require tailored solutions to meet unique performance and reliability requirements. For instance, industries such as aerospace and defense may rely on specialized ECC memory designed to withstand extreme conditions and ensure data integrity in critical missions.

The transition from DDR4 to DDR5 is expected to be a gradual process, with both technologies coexisting for some time. Organizations with existing DDR4 infrastructure may opt for incremental upgrades, while new deployments are likely to favor DDR5 for its advanced capabilities. This transition period presents opportunities for memory manufacturers