On the public citation graph
Estimation of High-Resolution Emissions Using Pure Nighttime Lights in Monocentric and Polycentric Cities: Fusion of Remotely Sensed Human Activities and Social Media Data for Carbon Neutral Settlements
0 signed · 62 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.apenergy.2020.116400openalex-extracted
Spatial and structural characteristics of CO2 emissions in East Asian megacities and its indication for low-carbon city development
inferredcites →https://doi.org/10.1016/j.enbuild.2020.110376openalex-extracted
Developing a high-resolution emission inventory tool for low-carbon city management using hybrid method – A pilot test in high-density Hong Kong
inferredcites →https://doi.org/10.1016/j.enpol.2020.111438openalex-extracted
Carbon emission quantification and decarbonization policy exploration for the household sector - Evidence from 51 Japanese cities
inferredcites →https://doi.org/10.1016/j.enpol.2022.113002openalex-extracted
Beyond automobility? Lock-in of past failures in low-carbon urban mobility innovations
inferredcites →https://doi.org/10.1016/j.envpol.2021.117666openalex-extracted
Anthropogenic emission inventory of multiple air pollutants and their spatiotemporal variations in 2017 for the Shandong Province, China
inferredcites →https://doi.org/10.1016/j.foreco.2019.117639openalex-extracted
Assessing structural changes at the forest edge using kernel density estimation
inferredcites →https://doi.org/10.1016/j.isprsjprs.2021.07.003openalex-extracted
Urban scene understanding based on semantic and socioeconomic features: From high-resolution remote sensing imagery to multi-source geographic datasets
inferredcites →https://doi.org/10.1016/j.jag.2019.101989openalex-extracted
Analyzing parcel-level relationships between Luojia 1-01 nighttime light intensity and artificial surface features across Shanghai, China: A comparison with NPP-VIIRS data
inferredcites →https://doi.org/10.1016/j.jag.2021.102475openalex-extracted
Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method
inferredcites →https://doi.org/10.1016/j.jag.2022.103023openalex-extracted
Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area
inferredcites →https://doi.org/10.1016/j.jclepro.2018.01.131openalex-extracted
Residential carbon dioxide emissions at the urban scale for county-level cities in China: A comparative study of nighttime light data
inferredcites →https://doi.org/10.1016/j.jclepro.2019.118840openalex-extracted
Influencing factors and spatial patterns of energy-related carbon emissions at the city-scale in Fujian province, Southeastern China
inferredcites →https://doi.org/10.1016/j.jclepro.2020.123411openalex-extracted
Polycentric structure and carbon dioxide emissions: Empirical analysis from provincial data in China
inferredcites →https://doi.org/10.1016/j.jclepro.2020.123427openalex-extracted
Estimating spatiotemporal dynamics of county-level fossil fuel consumption based on integrated nighttime light data
inferredcites →https://doi.org/10.1016/j.jclepro.2022.131944openalex-extracted
Investigating the spatiotemporal changes and driving factors of nighttime light patterns in RCEP Countries based on remote sensed satellite images
inferredcites →https://doi.org/10.1016/j.jclepro.2022.132301openalex-extracted
Spatio-temporal dynamic evolution of carbon emission intensity and the effectiveness of carbon emission reduction at county level based on nighttime light data
inferredcites →https://doi.org/10.1016/j.jclepro.2022.132547openalex-extracted
Spatial-temporal evolution and influencing factors of net carbon sink efficiency in Chinese cities under the background of carbon neutrality
inferredcites →https://doi.org/10.1016/j.jclepro.2022.133441openalex-extracted
Consumer perceptions and attitudes towards climate information on food
inferredcites →https://doi.org/10.1016/j.jclepro.2022.134272openalex-extracted
High-resolution temporal and spatial evolution of carbon emissions from building operations in Beijing
inferredcites →https://doi.org/10.1016/j.jclepro.2022.134932openalex-extracted
Impact of circular measures to reduce urban CO2 emissions: An analysis of four case studies through a production- and consumption-based emission accounting method
inferredcites →https://doi.org/10.1016/j.jclepro.2022.134936openalex-extracted
The perceived value of environmental sustainability for consumers in the air travel industry: A choice-based conjoint analysis
inferredcites →https://doi.org/10.1016/j.jes.2020.04.025openalex-extracted
Will polycentric cities cause more CO2 emissions? A case study of 232 Chinese cities
inferredcites →https://doi.org/10.1016/j.renene.2022.11.026openalex-extracted
Assessment of renewable energy, financial growth and in accomplishing targets of China's cities carbon neutrality
inferredcites →https://doi.org/10.1016/j.resconrec.2021.105706openalex-extracted
Spatially explicit carbon emissions at the county scale
inferredcites →https://doi.org/10.1016/j.resconrec.2021.106000openalex-extracted
High-resolution combined building stock and building energy modeling to evaluate whole-life carbon emissions and saving potentials at the building and urban scale
inferredcites →https://doi.org/10.1016/j.rse.2017.01.005openalex-extracted
Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics
inferredcites →https://doi.org/10.1016/j.rse.2019.111357openalex-extracted
Anisotropic characteristic of artificial light at night – Systematic investigation with VIIRS DNB multi-temporal observations
inferredcites →https://doi.org/10.1016/j.rse.2019.111430openalex-extracted
Characterizing urban infrastructural transitions for the Sustainable Development Goals using multi-temporal land, population, and nighttime light data
inferredcites →https://doi.org/10.1016/j.rse.2019.111443openalex-extracted
Remote sensing of night lights: A review and an outlook for the future
inferredcites →https://doi.org/10.1016/j.rse.2020.112201openalex-extracted
Mapping ice cliffs on debris-covered glaciers using multispectral satellite images
inferredcites →https://doi.org/10.1016/j.rse.2021.112648openalex-extracted
Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis
inferredcites →https://doi.org/10.1016/j.rse.2022.112962openalex-extracted
A classification-based spatiotemporal adaptive fusion model for the evaluation of remotely sensed evapotranspiration in heterogeneous irrigated agricultural area
inferredcites →https://doi.org/10.1016/j.rse.2022.113076openalex-extracted
Fusing Earth observation and socioeconomic data to increase the transferability of large-scale urban land use classification
inferredcites →https://doi.org/10.1016/j.rse.2022.113239openalex-extracted
Quantifying post-fire shifts in woody-vegetation cover composition in Mediterranean pine forests using Landsat time series and regression-based unmixing
inferredcites →https://doi.org/10.1016/j.scib.2019.04.036openalex-extracted
Urban sprawl in provincial capital cities in China: evidence from multi-temporal urban land products using Landsat data
inferredcites →https://doi.org/10.1016/j.scitotenv.2017.11.360openalex-extracted
A new method to quantify surface urban heat island intensity
inferredcites →https://doi.org/10.1016/j.scitotenv.2018.12.276openalex-extracted
Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.134394openalex-extracted
Multiscale analysis on spatiotemporal dynamics of energy consumption CO2 emissions in China: Utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.139148openalex-extracted
Improving population mapping using Luojia 1-01 nighttime light image and location-based social media data
inferredcites →https://doi.org/10.1016/j.scitotenv.2020.143869openalex-extracted
Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014–2016
inferredcites →https://doi.org/10.1016/j.scitotenv.2021.150037openalex-extracted
Intraurban heterogeneity of space-time land surface temperature trends in six climate-diverse cities
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.153718openalex-extracted
User behavior, influence factors, and impacts on real-world pollutant emissions from the household heating stoves in rural China
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.154662openalex-extracted
Direct observations of CO2 emission reductions due to COVID-19 lockdown across European urban districts
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.157630openalex-extracted
Using a Combination of Nighttime Light and Modis Data to Estimate Spatiotemporal Patterns of Co2 Emissions at Multiple Scales
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.159502openalex-extracted
Scaling of Chinese urban CO2 emissions and multiple dimensions of city size
inferredcites →https://doi.org/10.1016/j.scitotenv.2022.159588openalex-extracted
Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019
inferredcites →https://doi.org/10.1016/j.scs.2021.103600openalex-extracted
Spatial structure and carbon emission of urban agglomerations: Spatiotemporal characteristics and driving forces
inferredcites →https://doi.org/10.1016/j.scs.2022.104027openalex-extracted
Carbon sequestration and storage potential of urban residential environment – A review
inferredcites →https://doi.org/10.1016/j.scs.2022.104028openalex-extracted
Exploring differentiated impacts of socioeconomic factors and urban forms on city-level CO2 emissions in China: Spatial heterogeneity and varying importance levels
inferredcites →https://doi.org/10.1016/j.scs.2022.104288openalex-extracted
Regional disparities and evolution trend of city-level carbon emission intensity in China
inferredcites →https://doi.org/10.1016/j.trd.2018.08.006openalex-extracted
Urban form and spatial structure as determinants of the ecological footprint of commuting
inferredcites →https://doi.org/10.1016/j.uclim.2022.101174openalex-extracted
Economic agglomeration and emissions reduction: Does high agglomeration in China's urban clusters lead to higher carbon intensity?
inferredcites →https://doi.org/10.1016/j.uclim.2022.101223openalex-extracted
Does polycentric development produce less transportation carbon emissions? Evidence from urban form identified by night-time lights across US metropolitan areas
inferredcites →https://doi.org/10.1016/j.uclim.2022.101337openalex-extracted
Relationships between carbon emissions and urban population size and density, based on geo-urban scaling analysis: A multi‑carbon source empirical study
inferredcites →https://doi.org/10.1029/2020jd032974openalex-extracted
The Vulcan Version 3.0 High‐Resolution Fossil Fuel CO 2 Emissions for the United States
inferredcites →https://doi.org/10.1109/igarss39084.2020.9323255openalex-extracted
Use Night Time Light Remote Sensing to Discover Dragon Fruit Plantations in Vietnam
inferredcites →https://doi.org/10.1117/12.2532525openalex-extracted
Delimitation of urban systems by Luojia 1-01 night-time light imagery
inferredcites →https://doi.org/10.3390/ijgi8120555openalex-extracted
Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing
inferredcites →https://doi.org/10.3390/rs13081574openalex-extracted
Comparing Luojia 1-01 and VIIRS Nighttime Light Data in Detecting Urban Spatial Structure Using a Threshold-Based Kernel Density Estimation
inferredcites →https://doi.org/10.3390/rs14133148openalex-extracted
Using Remote Sensing to Identify Urban Fringe Areas and Their Spatial Pattern of Educational Resources: A Case Study of the Chengdu-Chongqing Economic Circle
inferredcites →https://doi.org/10.3390/su11174540openalex-extracted
Carbon Emissions Peak Prediction and the Reduction Pathway in Buildings during Operation in Jilin Province Based on LEAP
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.