Reduction Policies Research Articles

Characterization and sources of carbonaceous aerosol in ambient PM1 in Qingdao, a coastal megacity of northern China from 2017 to 2022

Organic carbon (OC) and elemental carbon (EC) are major chemical constituents of ambient submicron particles (PM1) and have substantial implications for air quality, climate dynamics, and human health. Long-term observations of daily mean mass concentrations of OC and EC in PM1 were conducted in Qingdao, a typical coastal megacity in northern China, in autumn and winter from Nov. 2017 to Jan. 2023. The variety in OC and EC concentrations under the active emission reduction policies of long-term carbon reduction in China, combined with the impact of the COVID-19 pandemic, were analyzed. OC and EC concentrations decreased noticeably from 8.28 ± 4.59 μg m−3 and 2.00 ± 1.17 μg m−3 during pre-COVID-19 pandemic to 6.34 ± 3.82 μg m−3 and 1.87 ± 1.01 μg m−3 during post-COVID-19 pandemic, respectively. The XGBoost-SHAP model indicated that emission sources had the most significant effect on OC and EC concentrations, followed by meteorological factors. Unfavorable meteorological conditions and a substantial increase in emissions are responsible for the increase of OC and EC concentration in winter. Potential source contribution function results indicated that the southern, eastern, and central Shandong regions were the primary source areas for transporting high concentrations of carbonaceous components. Positive matrix factorization analysis suggested that carbonaceous aerosols in Qingdao predominantly originated from coal/biomass combustion (54.75%–81.78%) and local vehicle emissions (8.72%–28.73%). Compared with that before COVID-19, coal/biomass combustion's contribution to OC and EC decreased by 19% after the COVID-19 pandemic. The contribution of vehicles increased by 110%, possibly due to the increase in motor vehicles and the rapid recovery in diesel consumption in freight transportation. Prohibiting biomass burning, such as open burning of straw, and measures such as promoting low-carbon and clean production of coal-fired industrial furnaces might have played a key role in reducing OC and EC levels. These results contribute to a deeper understanding of the concentration levels and sources of carbonaceous components in PM1 and provide theoretical guidance for environmental management.

Predictive modeling of regional carbon storage dynamics in response to land use/land cover changes: An InVEST-based analysis

Assessment of carbon stock (CS) in various land use/land cover (LULC) types is essential for environmental policies focused on reducing CO2 emissions and mitigating climate change. This study utilized the CA-Markov model to simulate future LULC scenarios and the InVEST model to evaluate CS changes in Pakistan from 2001 to 2030. The study employed two decades of yearly composite land cover data from MODIS, achieving high accuracy with a kappa value of 0.856. The results indicate that an increase of 38.1 × 103 km2 in cultivated land could lead to an increment of 13.5 Tg in Pakistan's total CS. In comparison, an increase in forest area can be the reason for raising above-ground carbon (AGC) by 16.8 Tg. These findings enhance the understanding of long-term LULC and CS changes in Pakistan. The study provides valuable insights for governments to refine land use strategies, adjust carbon emission reduction policies, and design better regulations based on the study's findings. Key recommendations include promoting vertical urban development to preserve carbon sequestration areas, implementing strict agricultural zoning laws, expanding afforestation initiatives like the Billion Tree Tsunami and Green Pakistan, and establishing a national LULC and CS monitoring program. Integrating data from various sources will create a comprehensive database to inform policy decisions and land management practices, contributing to global climate change mitigation efforts.

Spatial–temporal evaluation of total-factor energy efficiency in Chinese construction industry based on three-stage super-efficiency SBM-DEA model

PurposeThe study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.Design/methodology/approachUtilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.FindingsAnalysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.Originality/valueThis research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

Carbon emissions cap or energy technology subsidies? Exploring the carbon reduction policy based on a multi-technology sectoral DSGE model

As global climate change becomes increasingly severe, energy technology innovation has become a key means of coping with the climate crisis and realizing green and low-carbon development. However, existing literature rarely examines the effects of carbon emission reduction policies based on the perspective of energy technology progress for both short-term economic fluctuations and long-term equilibrium. This paper introduces the fossil energy technology sector and the renewable energy technology sector into the dynamic stochastic general equilibrium (DSGE) model, and compares the effectiveness of the carbon emission cap policy, the fossil energy technology subsidy policy, and the renewable energy technology subsidy policy under the framework of China’s carbon trading market in promoting macroeconomic growth and controlling pollutant emissions. We found that in long-term, the emission reduction effect of the carbon emission cap policy falls short of the other two policies, and subsidizing fossil fuel technologies is more cost-efficient comparatively. The government expenditure shock can all stimulate macroeconomic growth with crowding out of private investment and household consumption, whereas the energy technology research productivity shock leads to a decline in total output and an increase in renewable energy technology demand. In addition, pollutant emissions are pro-cyclical under the impact of total carbon policies and counter-cyclical under the impact of energy technology subsidy policies. This article constructs a multi-technology sectoral dynamic stochastic general equilibrium model, expanding the research perspective and theoretical framework for evaluating carbon emission reduction policies. At the same time, it proves the importance of the government to implement the phased energy technology subsidy policy while implementing the carbon emission cap policy, which provides important enlightenment for the implementation and adjustment of carbon emission reduction policy.

Mainstreaming ecosystem-based approaches into disaster risk reduction policies: a comparative study of Nepal, India, and Bangladesh

Humanity faces a rising number of challenging disasters due to their increasing unpredictability and scale. While large-scale engineering solutions have been the mainstream approach, there is a growing acknowledgment that ecosystem-based disaster risk reduction (Eco-DRR) is a suitable approach in the long term due to its cost-effectiveness, social equity, and environmental sustainability. Governments worldwide are integrating Eco-DRR into policies and legislation. Here, we review the extent of integration of Eco-DRR into DRR policies and legislation in Nepal, India, and Bangladesh using content analysis based on five criteria: acknowledgment, inclusion, weighting, resources, and reflexivity. While Eco-DRR principles are acknowledged and included in the national policies of these three countries, they are not prioritized as compared to competing alternatives due to limited integration tools. Resource allocation is directed towards prevention and relief mitigation, with limited emphasis on research and capacity building, which are crucial for strengthening Eco-DRR. Notably, policy aspirations still need to be realized in legislative action. Strengthening Eco-DRR requires enhanced integration, capacity-building, and robust institutions to boost resilience.

Do green and low-carbon development policies affect carbon markets? Evidence from China

ABSTRACT Responses of carbon prices to emission reduction policies can imply the market incentive effect of certain policies and effectiveness of carbon markets. The article collects 51 policy events related to green and low-carbon development implemented by the Chinese government from December 2013 to December 2023. Applying the event study method, it discusses the short-term reaction of carbon prices in China’s regional carbon markets to individual events, classified events, and events as a whole. In addition, five diplomatic statements of the Chinese government in multilateral climate governance process are selected, and a panel regression model is constructed to analyse the long-term reaction of carbon prices to each statement. The results are as follows: First, statements of the Chinese government in climate diplomacy have long-term incentive effect on carbon markets. Second, China’s green and low-carbon development policies have brought significant market incentives overall. Among the national, regional and industrial policies, the national and industrial policies have the most significant positive influence on carbon prices in the short term. Third, Hubei, Fujian and Shenzhen carbon markets are more effective. China should actively participate in multilateral climate diplomacy and climate governance, establish a more detailed low-carbon policy system, and improve the scale and activity of carbon markets.

Carbon emission efficiency of China’s construction industry based on super-efficiency SBM and Tobit model

Global warming is one of the key issues attracting international concern. The carbon dioxide emission produced by energy combustion is the main cause of the greenhouse effect, and reducing carbon emissions is considered the most effective way to deal with the greenhouse effect. The extensive production mode characterized by high energy consumption, high emission, and low efficiency in China’s construction industry intensifies the contradiction between economic development and resources and the environment, and the growth under this mode is at the expense of consuming a lot of resources and energy. The improvement of carbon emission efficiency is an effective means of achieving the goal of economic growth and carbon emission reduction simultaneously, making it necessary to accurately measure the carbon emission efficiency of the construction industry in each province, determine the influencing factors, and formulate reasonable emission reduction policies for this industry. In this study, an input-output index system of carbon emission efficiency of China’s construction industry was constructed, the carbon emission efficiency of the construction industry in each province was evaluated using the super-efficiency SBM model, and the factors affecting the carbon emission efficiency of this industry were analyzed via the Tobit model. The results showed that the average value of carbon emission efficiency of the construction industry generally showed a rising trend in a fluctuating way during the study period. From 2014 to 2022, the average carbon emission efficiency of the national construction industry presented an upward trend, from 1.122 in 2014 to 1.148 in 2022; the regional economic level (p= 0.020 < 0.05) and human capital level (p= 0.000 < 0.01) exerted obvious promoting effects on the carbon emission efficiency of China’s construction industry, while the urbanization development (p= 0.049 < 0.05) generated evident negative effects on carbon emission efficiency of this industry. The research results have important reference values for making cross-provincial emission reduction plans for the construction industry, promoting its carbon emission efficiency, and driving the research and development of green building materials and clean energy.

Combined short-term and long-term emission controls improve air quality sustainably in China

The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by “blue skies” type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action.

Nitrous oxide emissions and microbial communities variation in low dissolved oxygen and low carbon-to-nitrogen ratio anoxic-oxic wastewater treatment plant.

Dissolved oxygen (DO) levels and carbon-to-nitrogen (C/N) ratio affect nitrous oxide (N2O) emissions by influencing the physiological and ecological dynamics of nitrifying and denitrifying microbial communities in activated sludge systems. For example, Nitrosomonas is a common N2O producing nitrifying bacteria in wastewater treatment plants (WWTPs), and DO conditions can affect the N2O production capacity. Previous studies have reported N2O emission characteristics under adequate DO and C/N conditions in A/O WWTPs. However, in actual operation, owing to economic and managerial factors, some WWTPs have a long-term state of low DO levels in oxic tanks and low influent C/N. Research on N2O emission characteristics in low DO-limited and low C/N ratio WWTPs is limited. This study investigated N2O emissions and the corresponding shifts in microorganisms within an anoxic-oxic (A/O) WWTP over 9-month. Quantitative PCR was used to assess the abundance of ten functional genes related to nitrification and denitrification processes, and high-throughput sequencing of the 16S rRNA gene was employed to determine the composition change of microorganisms. The findings revealed that 1) the average N2O emission factor was 1.07% in the studied WWTP; 2) the DO-limited oxic tank primarily contributed to N2O; 3) NO2-, TOC, and C/N ratios were key factors for dissolved N2O in the aerobic tank; and 4) Nitrosomonas and Terrimonas exhibited a robust correlation with N2O emissions. This research provides data references for estimating N2O emission factors and developing N2O reduction policies in WWTPs with DO-limited and low C/N ratios.

Unpacking the path-dependent process of academic burden reduction policy in China (1978–2021)

Unpacking the path-dependent process of academic burden reduction policy in China (1978–2021)

The time-advance effect of China's rooftop solar photovoltaics program on the dual carbon targets and its implication on the globe

Rooftop solar photovoltaics (RSPV) plays an important role in energy transition and climate goals. However, the contribution of RSPV to the dual carbon targets (DCTs) has not yet been quantitatively investigated at the national or global scale. Here, we investigate this contribution with an improved Stochastic Impacts by Regression on the Population, Affluence, and Technology (STIRPAT) model. We find out the time-advance effect of China's pilot RSPV program, i.e., doubling expansion of the current pilot area helps that the DCTs will be achieved 5 years ahead of schedule; a full expansion plan from merely six identified key provinces can be enough to guarantee that China will achieve carbon peak and carbon neutrality 5 and 4 years ahead of their respective time schedules. This effect implies global RSPV development; about 25 % of the world's buildable rooftops for RSPV installation will help reduce 1.4 billion tons of carbon emissions per year and thus achieve global carbon neutrality by 2050. The results will attract a widespread new energy community interested in expanding RSPV and helping governments formulate carbon reduction policies.

Working patterns, leisure dynamics, and energy consumption: a time-use analysis for Italy

ABSTRACT Recent working time reduction policies aim at achieving a triple dividend, yet their impact is uncertain due to potential rebound effects. Saved work time may be spent on energy-intensive leisure activities, complicating the overall outcomes. In our study, we conduct a time use analysis to investigate how different work schedules affect leisure time and its associated energy consumption, using the Italian Time Use Survey from Istat. Our analysis examines how predictive factors such as employment status, gender, and variables related to the household environment affect preferences for leisure activities, employing various econometric models, including Dirichlet regression which best suits compositional data. Results suggest that working time predominantly affects leisure time allocation; employment status, gender, and the presence of a teenager in the household also play a role. From an energy usage standpoint, a time rebound effect is present, although it is not mediated by the aforementioned factors.

The nexus between natural resource development, trade policy uncertainty, financial technology and poverty in China: Contributing to the realization of SDG 1

Population poverty inhibits China's economic growth. Reducing poverty in the face of risk and uncertainty has been a priority for the Chinese government in recent years. And it is an important part of realizing the Sustainable Development Goal 1. The paper first uses entropy method and TOPSIS to comprehensively evaluate the Financial Technology (Fintech) index. This paper uses the autoregressive distributed lag (ARDL) bounds test to measure the long-term relationship. This paper also uses a nonlinear autoregressive distributed lag (NARDL) model to examine the asymmetric impact of trade policy uncertainty (TPU), natural resource development (NRD) and fintech on poverty reduction. For the robustness of the model, this paper uses three parametric methods FMOLS, DOLS and CCR. The findings of this paper are as follows. Increases or decreases in TPU can have a negative impact on poverty reduction. It brings greater impact when it increases. A reduction in the level of NRD and an increase in the fintech index will promote poverty reduction policies. An increase in the level of NRD and a decrease in the fintech index have opposite effects. The results of FMOLS, DOLS and CCR support these conclusions. The findings of this research provide effective suggestions for the formulation of future poverty eradication policies and the promotion of fintech development in China.

Public attention and psychological trends towards waste reduction: A large-scale data analysis based on social media

In the digital era, using social media big data to capture the true views and psychological trends shown by the public regarding waste reduction could considerably improve the formulation of targeted waste reduction policies and guide residents to participate in environmental governance from the source side. This study used big data mining technology to trawl 617,771 pieces of waste reduction text from a typical social media site (Sina Microblog). A machine learning algorithm model was used to identify the psychological and cognitive focus of the public and their preferences based on large-scale text data. The temporal and spatial differences in public attention trends, hot topic trends, and sentiment trends were also investigated. The results showed that the public attention level was related to the release of policies by government and that public attention in the southeast coastal areas was higher than that in the northwest inland areas. Moreover, waste reduction had a “working attribute” because the public paid more attention to waste reduction during working hours (i.e., 9:00–12:00 and 15:00–18:00) than during leisure hours. In terms of individual heterogeneity, males were initiators of the topic, whereas females were followers. In particular, participation by young people in waste reduction discussions was higher than for other groups. The topic analysis showed that public attention followed a cooperative construction pattern that had multiple entities, including the individual, community, and government. Overall, the public sentiment score towards waste reduction increased year by year during the study period, with positive sentiment posts accounting for over 70% of the total number of blog posts, and that the vast majority of residents had a positive attitude towards waste reduction. This study expanded current research knowledge by exploring the public attitude response to waste reduction from a social media perspective. The study will help the government to effectively intervene in public behavior tendencies toward waste reduction from the psychological perspective and provided important implications about how the government can enhance its use of social media to effectively guide public opinion and improve policies.

Assessing the economic implications of carbon emissions on climate change: Estimating the impact using methane-adjusted DICE model

This study aims to highlight the importance of accurately estimating methane emissions, one of the most dangerous and important greenhouse gases in the context of climate change. By incorporating methane emissions as a variable within the integrated assessment model DICE (Dynamic Integrated Climate-Economy model), we investigate how these emissions influence temperature changes and subsequently impact economic policies, including climate economic policies, carbon pricing, and non-price factors. We use the existing DICE-2020 and DICE-2023 models as references for our analysis. In addition to industrial emissions that can be detected through satellite observations, we address the challenge of estimating natural emissions from wetlands and permafrost, which leak gradually and are difficult to detect. By considering these emissions, we account for their exogenous nature and their divergence from the current situation. Our study reveals that incorporating methane emissions into the DICE model has significant implications for global temperature outcomes and subsequent policy changes. We find that by implementing existing methane reduction policies, which includes cutting the level of methane emissions in half and increase the carbon price in 4 times to 500 USD per ton, it is possible to achieve the more ambitious goal of limiting the global temperature increase to 1.5 °C by 2100 in an optimistic scenario instead of the common target of 2 °C. More pessimistic scenarios that do not imply big change in methane emissions, but the same numerical data for carbon price, still suggests the possibility of keeping the global temperature below 2 °C. The findings of this study emphasize the importance of estimating methane emissions in efforts to mitigate climate change. Recognizing the impact of methane on global temperature change, policymakers can make informed decisions regarding economic policies, carbon pricing, to effectively address the challenges posed by climate change. This research contributes to the field by incorporating methane emissions into the DICE model, providing a more complete understanding of its influence on global climate outcomes and economic implications. Additionally, by highlighting the potential benefits of methane reduction measures, this study provides information on how to achieve more ambitious climate targets.

The effect of enterprise quality management on innovation incentives in enterprises —— based on the reform of China's value-added tax system

This article will analyze the incentive effect of the value-added tax rate reduction policy on enterprise innovation from the perspective of enterprise quality management. The research results indicate that: (1) the reduction of value-added tax rate has an incentive effect on the innovation activities of enterprises; (2) There is a positive moderating effect of enterprise quality management on the reduction of value-added tax rate and the innovation and research and development activities of enterprises; (3) The reduction of value-added tax rate has a more significant incentive effect on innovation in the eastern region of China, state-owned enterprises, and relatively larger enterprises.

Influence of eye-related behavior on myopia among junior middle school students under the background of double reduction during the COVID-19 pandemic

BackgroundTo investigate the changes in the unhealthy eye-related behaviors of junior middle school students during the COVID-19 pandemic and the double reduction policy and its relationship with myopia.MethodsData were obtained from the 2019–2022 Tianjin Children and Youth Myopia, Common Diseases and Health Influencing Factors Survey. Latent profile analysis (LPA) and a generalized linear model (GLM) were applied to analyze the effect of eye-related behavior classes on myopia.ResultsA total of 2508 junior middle school students were included. The types of eye-related behavior were categorized into the medium-healthy behavior group, heavy academic burden and near-eye behavior group, insufficient lighting group and high-healthy behavior group. Students with heavy academic burdens and near-eye behavior were more likely to develop myopia than were those in the high-healthy group (OR = 1.466, 95% CI = 1.203–1.787; P < 0.001).ConclusionsThe dual reduction policy has a positive effect on improving unhealthy eye-related behaviors, and the prevention and control of myopia through the use of different combinations of eye-related behaviors are heterogeneous among junior middle school students. In the post-COVID-19 period, we should continue to implement a double reduction policy and formulate targeted eye-related behavior strategies to provide an important reference for the prevention and control of myopia among children and adolescents during public health emergencies in the future.

Efficiency of construction waste and carbon reduction in the construction industry: based on improved three stage SBM-DEA model in China

PurposeReducing construction waste generation and carbon emission in the construction industry is crucial for the “dual carbon” goal. Evaluating the efficiency of reducing construction waste generation and carbon emission in the construction industry at the regional level is an important evaluation basis for the sustainable development of the construction industry. It provides a basis for formulating construction waste and carbon reduction policies tailored to local conditions and comprehensively promote the sustainable development of the construction industry.Design/methodology/approachA three stage SBM-DEA model based on non-expected outputs is proposed by combining the SBM-DEA model with the SFA method. The proposed model is used to evaluate the efficiency of construction waste and carbon reduction in the construction industry in 30 regions of China from 2010 to 2020. Moreover, the study explores the impact of environmental variables such as urbanization level, proportion of construction industry employees, resident consumption level, and technological progress.FindingsFrom 2010 to 2020, the efficiency of construction waste and carbon reduction in China’s construction industry has been increasing year by year. Provinces with higher efficiency of construction waste and carbon reduction in the construction industry are mainly concentrated in the eastern coastal areas, showing an overall pattern of “East&gt;West&gt;Northeast&gt;Middle”. There is a clear correlation between the level of urbanization, the proportion of construction industry employees, residents’ consumption level, technological progress, labor input, machinery input, and capital investment. The construction waste and carbon emission efficiency of the construction industry in various provinces is greatly influenced by environmental factors.Practical implicationsThe research results provide policy makers and business managers with effective policies for reducing construction waste generation and carbon emission in the construction industry, especially circular economy policies. To provide empirical support for further understanding the connotation of construction waste and carbon reduction in the construction industry, to create innovative models for construction waste and carbon reduction, and to promote the multiple benefits of construction waste and carbon reduction in the construction industry, and to provide empirical support for countries and enterprises with similar development backgrounds in China to formulate relevant policies and decision-making.Originality/valueThe construction industry is a high investment, high energy consumption, and high pollution industry. This study uses the three stage SBM-DEA model to explore the efficiency of construction waste and carbon reduction in the construction industry, providing a new perspective for the evaluation of sustainable development in the construction industry, enriching and improving the theory of sustainable development.

Attributing the Decline of Evapotranspiration over the Asian Monsoon Region during the Period 1950–2014 in CMIP6 Models

Evapotranspiration (ET) accounts for over half of the moisture source of Asian monsoon rainfall, which has been significantly altered by anthropogenic forcings. However, how individual anthropogenic forcing affects the ET over monsoonal Asia is still elusive. In this study, we found a significant decline in ET over the Asian monsoon region during the period of 1950–2014 in Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The attribution analysis suggests that anthropogenic aerosol forcing is the primary cause of the weakening in ET in the historical simulation, while it is only partially compensated by the strengthening effect from GHGs, although GHGs are the dominant forcings for surface temperature increase. The physical mechanisms responsible for ET changes are different between aerosol and GHG forcings. The increase in aerosol emissions enhances the reflection and scattering of the downward solar radiation, which decreases the net surface irradiance for ET. GHGs, on the one hand, increase the moisture capability of the atmosphere and, thus, the ensuing rainfall; on the other hand, they increase the ascending motion over the Indian subcontinent, leading to an increase in rainfall. Both processes are beneficial for an ET increase. The results from this study suggest that future changes in the land–water cycle may mainly rely on the aerosol emission policy rather than the carbon reduction policy.

Reporting of and policy on greenhouse gas emissions from air travel at Canadian universities

Purpose This paper aims to explore the extent and characteristics of Canadian university reporting of and policy regarding greenhouse gas emissions from air travel. It identifies current approaches’ details and limits and recommends improvements. Design/methodology/approach The study developed questions and considerations for analysing reporting and policy, reviewed university documents and webpages and contacted university staff. Findings Roughly 20% of Canadian universities report flight emissions. Figures vary by factors of over 100 even when normalized or expressed as a percentage of institutional emissions. Inter-university differences in data collection and emissions calculation practices shape reporting. Canadian university air travel emissions cannot be meaningfully compared. Few universities have flight emissions reduction policies; those that do leave relevant decisions to individuals. These approaches do not respond adequately to the emissions reduction challenge. Originality/value This study is the first comprehensive survey of university flight emissions reporting for any country. Its original framework highlights calculation’s complexities. It recommends standardizing reporting process information disclosure, reporting flight emissions as a range and faculty leadership of emissions reduction efforts.