Population Scale Effect Research Articles

Study on carbon emission characteristics and its influencing factors of energy consumption in Sichuan Province, China

Clarifying the key sectors and influencing factors of carbon emissions from energy consumption is an important prerequisite for achieving the “carbon peaking and carbon neutrality” goals. This study calculated the carbon emissions of fuel combustion in 7 major departments and regional electricity trading of Sichuan Province from 2000 to 2021, and empirically analyzed the impact of energy structure effect, energy intensity effect, industrial structure effect, economic development level effect, and population size effect on the carbon emissions of energy consumption based on the LMDI model. The main research conclusions are as follows: (1) LMDI model has the advantages of no residual and high interpretation. By refining the multi-departments of energy consumption and different types of fuel, it is helpful to improve the accuracy of empirical analysis results. (2) The carbon emissions of energy consumption mainly come from the fuel combustion process. Specifically, the industry sector composed of steel, building materials, chemicals and machinery is the key emission sector, and transportation and residential life are also vital. Regional electricity trading can indirectly reduce the intensity of carbon emissions while ensuring the safety of energy supply. (3) From 2000 to 2021, the energy intensity effect and the economic development level effect were key factors in slowing down and promoting the carbon emission growth of energy consumption in Sichuan Province, respectively. The population scale effect mainly played a positive role in driving carbon emissions, but the impact is small and almost negligible. Before 2012, the energy structure effect and the industrial structure effect were mainly positive driving effects, and after 2012, they all turned into negative inhibitory effects. This was mainly due to the low-carbon transformation of energy structure and the optimization of industrial structure.

Dynamics of urban expansion and form changes impacting carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area counties

Cities are the main carriers of social and economic development, and they are also important sources of carbon emissions. Therefore, it is essential to explore the impact of urban expansion and form changes on carbon emissions. Here, we attempted to analyzes the relationship between urban expansion and carbon emissions at the county level in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1997 to 2017. It further decomposes the driving effects of carbon emissions from multiple factors, and considers the spatial heterogeneity between different urban form changes and driving effects. The results show that: The relationship between urban expansion and carbon emissions in the GBA has gone through three stages from 1997 to 2017, with 2012 as a turning point. Optimization of economic development models and strict protection of the ecological environment can effectively control carbon emissions. After 2012, the economic development effect (GE) and population scale effect (PE) are the driving factors of carbon emissions, while the carbon emission intensity effect (CE) and urban land intensity effect (UE) are the inhibitory factors of carbon emissions. The contribution rate of UE to carbon emission reduction can reach 86 %. The impact of urban form changes on carbon emissions has spatial heterogeneity. The changes in urban form have a significant impact on the carbon emissions of counties in Dongguan and Shenzhen. The increase in fragmentation indirectly promotes carbon emissions. In 2007–2012, the increase in centrality significantly weakened the economic development effect, which is conducive to emission reduction. After 2007, the increase in compactness in counties in the eastern part of the GBA, including Zhongshan and Zhuhai, is not conducive to emission reduction.

Analysis of Ningxia Hui Autonomous District’s Gray Water Footprint from the Perspective of Water Sustainability

Gray water footprint (GWF) is an effective method to evaluate the degree of water pollution and water quality. It is the amount of freshwater needed to dilute water pollutants to meet ambient water quality standards. Accounting and analyzing the GWF will be significant for promoting an improved water environment and sustainable water ecology in Ningxia Autonomous District. We accounted for the GWF of all cities in Ningxia from 2012 to 2020 and evaluated its spatial-temporal variations by the GWF accounting method proposed by Hoekstra. Then, the Logarithmic Mean Divisia Index (LMDI) method was applied to investigate the contributions of four driving factors: the population scale effect, economic development effect, technological effect, and industrial structure effect. And then, the changes in the GWF in the Ningxia region were analyzed. The results showed that the GWF in the Ningxia region changed from 79.21 × 108 to 29.09 × 108 m3/yr during 2012–2020, making a significant decreasing trend. Among all cities, Wuzhong City contributes the most in terms of the GWF. More specifically, economic development and technology structure are the positive and negative drivers of the GWF, respectively. The water pollution levels in Ningxia (0.49–1.3) indicated that the waste assimilation capacity has fallen short of taking up the pollutant load, which had an unfavorable impact on the groundwater according to actual water quality data. NO3-N and NH3-N are detected in the groundwater throughout the Ningxia region, with the highest NH3-N content in the groundwater in Yinchuan, which almost exceeded the groundwater quality standard of category III. Above all, this study reflected the current water pollution situation better by combining the GWF with actual water quality data in Ningxia. The finding of this study is valuable for addressing water quality threats and developing sustainable development.

Analysis of Spatial and Temporal Evolution Characteristics and Influencing Factors of the Water Footprint in Xinjiang from 2000 to 2020

Xinjiang is home to one of the most serious resource-based water shortages, and at the same time, it is an important main production area of grain, cotton, and high-quality fruits and vegetables in China, placing a heavy burden on water resources. Based on this, this paper determines the basic condition of water resources in regions of Xinjiang using the water footprint method. It then identifies the drivers of water footprint changes using the population scale effect, policy support effect, investment–output effect, economic structure effect, water use efficiency effect, and water use structure effect via the LMDI decomposition model. Finally, this paper illustrates the trajectory of the regional water footprint through individual stochastic convergence. This study found the following: (1) The water footprint of Xinjiang showed a fluctuating upward trend, and the total water footprint varied significantly between regions. From a compositional standpoint, most regions were dominated by the agricultural water footprint, while spatially, the regional water footprint had a high distribution trend in the south and a low distribution in the north. (2) The driving effects of the water footprint, policy support, population scale, and water use structure were incremental, while the effects of water use efficiency, economic structure, and investment output were decremental. (3) Most regions in Xinjiang showed individual stochastic convergence trends, indicating that regions converged to their respective compensating difference equilibrium levels. In this regard, it is necessary to strengthen R&D and the promotion of water use technology, further optimize the industrial structure, and leverage the positive effect of government investment to alleviate the regional water constraint dilemma and promote high-quality regional economic development.

Public Health Impacts of Vaccines for COVID-19 and Beyond: Opportunities to Overcome Technical and Regulatory Barriers for Randomized Trials.

The COVID-19 pandemic has revealed the importance of the population-scale effects of both diseases and interventions. Vaccines have had an enormous impact, greatly reducing the suffering caused by COVID-19. Clinical trials have focused on individual-level clinical benefits, however, so the broader effects of the vaccines on preventing infection and transmission, and their overall effect at the community level, remain unclear. These questions can be addressed through alternative designs for vaccine trials, including assessing different endpoints and randomizing at the cluster instead of individual level. Although these designs exist, various factors have limited their use as preauthorization pivotal trials. They face statistical, epidemiological, and logistical limitations as well as regulatory barriers and uncertainty. Addressing these hindrances through research, communication, and policy can improve the evidence base of vaccines, their strategic deployment, and population health, both in the COVID-19 pandemic and in future infectious disease outbreaks. (Am J Public Health. Published online ahead of print April 27, 2023:e1-e8. https://doi.org/10.2105/AJPH.2023.307302).

Spatiotemporal patterns and drivers of carbon emissions from municipal solid waste treatment in China

Reducing carbon emissions from municipal solid waste (MSW) treatment is non-negligible for China to meet its “carbon peaking and carbon neutrality” targets. It is critical to objectively evaluate the spatiotemporal patterns and drivers of carbon emissions from MSW treatment. This study estimates the carbon emissions from MSW treatment across 30 Chinese provinces from 2011 to 2020. The joint approach LMDI-PDA model is further used to refine the impact of policy on carbon emission changes from technical and efficiency perspectives, while considering the socio-economic factors. The results showed that carbon emissions from MSW treatment grew significantly until peaking at 202.05Mt CO2e in 2017 and then stabilized, finally dropping to 165.10 Mt CO2e in 2020 due to the impact of COVID-19. Compared with the “12th Five-Year Plan” period, the MSW emissions intensity declined significantly during the “13th Five-Year Plan” period, indicating the effective implementation of waste emission control measures. Furthermore, the slowdown in the growth of national emissions was primarily driven by technological advances in waste treatment. Technical efficiency change effect, MSW generation intensity effect, economic scale effect, and population scale effect impeded national emissions decline. Since the performance of various drivers varied greatly in different provinces, a cluster analysis was conducted to provide policy recommendations in provinces with similar characteristics. Both the methods and results of this study can provide better decision-making support for national and provincial carbon emissions control policies targeting MSW treatment.

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China.

As the largest energy consumer, China's control of carbon emissions from energy consumption plays a pivotal role in world climate governance. However, few studies have been conducted to explore the emission reduction pathways that promote a high level of synergy between China's economic growth and the " carbon peaking and carbon neutrality " goal from the perspective of energy consumption. Based on the measurement of energy consumption carbon emissions, this paper reveals the spatial and temporal distribution and evolution trends of carbon emissions in China at the national-provincial level. The multi-dimensional socio-economic factors such as R&D and urbanization are taken into account, and the LMDI model is used to decompose the driving effects of energy consumption carbon emissions at the national-provincial levels. Further, this paper combines the Tapio decoupling index with the LMDI model to decompose the decoupling states of China year by year and at the provincial level in four periods to explore the reasons for the change of carbon decoupling states. The results show that: (1) China's energy consumption carbon emissions grew at a high rate before 2013, and slowed down after that. There are significant differences in the scale and growth rate of carbon emissions among provinces, which can be classified into four types accordingly. (2) The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect inhibit the growth of China's carbon emissions. (3) Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. According to the conclusions, this paper proposes targeted policy recommendations based on China's energy endowment.

Impacts of digital economy agglomeration on carbon emission: A two-tier stochastic frontier and spatial decomposition analysis of China

The digital economy agglomeration (DEAGG), which crucially influences global economic growth, is strongly correlated with carbon emissions performance. This study utilizes prefecture-level city data pertaining to China (sample period; 2006–2019), and it is based primarily on the new economic geography theory. The bilateral effects that DEAGG exerts on carbon emissions were initially investigated using a two-tier stochastic frontier analysis. Subsequently, the spatial econometric technique and the logarithmic mean Divisia index (LMDI) method were combined for a mechanism analysis. The empirical results indicated the following: (1) The agglomeration effect that DEAGG exerted on carbon emissions highly exceeds the spread effect, which leads to a negative net effect. (2) DEAGG reduces carbon emissions mainly by reducing the energy intensity effect and by inhibiting the population scale effect; moreover, the energy structure effect and the economic growth effect that are occasioned by DEAGG yield a hindering impact during the sample period. (3) Further analysis indicates that DEAGG narrows carbon inequality, and that it facilitates the realization of the co-benefits pertaining to air pollution mitigation. Therefore, to enhance DEAGG and environmental quality, we propose specific policy recommendations.

Sex-biased infections scale to population impacts for an emerging wildlife disease.

Demographic factors are fundamental in shaping infectious disease dynamics. Aspects of populations that create structure, like age and sex, can affect patterns of transmission, infection intensity and population outcomes. However, studies rarely link these processes from individual to population-scale effects. Moreover, the mechanisms underlying demographic differences in disease are frequently unclear. Here, we explore sex-biased infections for a multi-host fungal disease of bats, white-nose syndrome, and link disease-associated mortality between sexes, the distortion of sex ratios and the potential mechanisms underlying sex differences in infection. We collected data on host traits, infection intensity and survival of five bat species at 42 sites across seven years. We found females were more infected than males for all five species. Females also had lower apparent survival over winter and accounted for a smaller proportion of populations over time. Notably, female-biased infections were evident by early hibernation and likely driven by sex-based differences in autumn mating behaviour. Male bats were more active during autumn which likely reduced replication of the cool-growing fungus. Higher disease impacts in female bats may have cascading effects on bat populations beyond the hibernation season by limiting recruitment and increasing the risk of Allee effects.

How the epidemiology of disease-resistant and disease-tolerant varieties affects grower behaviour.

Population-scale effects of resistant or tolerant crop varieties have received little consideration from epidemiologists. When growers deploy tolerant crop, population-scale disease pressures are often unaffected. This only benefits growers using tolerant varieties, selfishly decreasing yields for others. However, resistant crop can reduce disease pressure for all. We coupled an epidemiological model with game theory to understand how this affects uptake of control. Each time a grower plants a new crop, they must decide whether to use an improved (i.e. tolerant/resistant) or unimproved variety. This decision is based on strategic-adaptive expectations in our model, with growers comparing last season’s profit with an estimate of what is expected from the alternative crop. Despite the positive feedback loop promoting use of a tolerant variety whenever it is available, a mixed unimproved- and tolerant-crop equilibrium can persist. Tolerant crop can also induce bistability between a scenario in which all growers use tolerant crop and the disease-free equilibrium, where no growers do. However, due to ‘free-riding’ by growers of unimproved crop, resistant crop nearly always exists in a mixed equilibrium. This work highlights how growers respond to contrasting incentives caused by tolerant and resistant varieties, and the distinct effects on yields and population-scale deployment.

Analysis of China's regional differences in water consumption based on spatial structural decomposition analysis model

Analyzing the factors that affect the spatial differences in production water consumption in China is necessary to implement its most stringent water resource management system effectively. Based on the input-output tables of 31 provinces and the water-consumption data of provinces by sectors in 2017, the water consumption-economy input-output tables of 31 provinces are constructed. The spatial structural decomposition analysis method is used to analyze the impact of technology level, economic scale, and regional characteristics on spatial differences in production water consumption. The final demand effect is then decomposed into final demand sectoral structural effect, final demand distribution structure effect, population-scale effect, and consumption-level effect. The results show that production water consumption depends on the economic scale and regional characteristics. Xinjiang, Jiangsu, Guangdong, Heilongjiang, and most provinces in the central region use more production water than the average level, while those in the Beijing-Tianjin region and most in the North-west region use less than average. The decomposition results show that the technical and the final demand effects are the main factors for the spatial differences. The impact of population-scale and consumption-level contribute the most to the final demand effect.

Carbon emission of China's power industry: driving factors and emission reduction path.

The low-carbon development of power industry is the key to low-carbon development of the whole society. In order to determine appropriate and feasible emission reduction policies, it is necessary to identify the contribution of different drivers to the change of carbon emissions in China’s power sector and to simulate the potential evolution trend of carbon emissions. This paper constructs LMDI model to analyze the driving factors of carbon emission changes in China’s power industry from 2000 to 2018 and uses Monte Carlo algorithm to simulate the evolution trend of carbon emission under different scenarios. We can find (1) economic output effect reached 3.817 billion tons from 2000 to 2018, which was the primary factor to increase the carbon emission. Population scale effect reached 251million tons, which had a weak promotion impact on carbon emission. (2) Conversion efficiency effect played a role in restraining carbon emissions, reaching 699 million tons from 2000 to 2018. (3) Emission factor effect and power intensity effect have obvious volatility. The power structure effect showed great volatility before 2013 and mainly played a role in restraining carbon emission after 2013. (4) Under the baseline scenario, the carbon emission of China’s power industry shows a growth trend. Under green development scenario and enhanced carbon reduction scenario, the carbon emission shows a trend of first increasing and then decreasing.

Population Genetic Structure Analysis Reveals Significant Genetic Differentiation of the Endemic Species Camellia chekiangoleosa Hu. with a Narrow Geographic Range

In order to protect and utilize the germplasm resource better, it is highly necessary to carry out a study on the genetic diversity of Camellia chekiangoleosa Hu. However, systematic research on population genetics analysis of the species is comparatively rare. Herein, 16 highly variable simple sequence repeat (SSR) markers were used for genetic structure assessment in 12 natural C. chekiangoleosa populations. The genetic diversity of C. chekiangoleosa was low (h = 0.596), within which, central populations (such as Damaoshan (DMS), Sanqingshan (SQS), and Gutianshan (GTS)) at the junction of four main mountain ranges presented high diversity and represented the center of the C. chekiangoleosa diversity distribution; the Hengshan (HS) population in the west showed the lowest diversity, and the diversity of the eastern and coastal populations was intermediate. C. chekiangoleosa exhibited a high level of genetic differentiation, and the variation among populations accounted for approximately 24% of the total variation. The major reasons for this situation are the small population scale and bottleneck effects in some populations (HS and Lingshan (LS)), coupled with inbreeding within the population and low gene flow among populations (Nm = 0.796). To scientifically protect the genetic diversity of C. chekiangoleosa, in situ conservation measures should be implemented for high-diversity populations, while low-diversity populations should be restored by reintroduction.

Analysis of the impact of the Beijing-Tianjin-Hebei coordinated development on environmental pollution and its mechanism.

Taking detailed rules for implementing of the plan on atmospheric pollution prevention and control in Beijing-Tianjin-Hebei region and surrounding areas promulgated and implemented in 2013 as the starting point, taking air pollution as the research object, using the propensity score matching method and difference-in-differences method to answer whether coordinated development of Beijing-Tianjin-Hebei is conducive to resolving the problem of air pollution. Using panel data from 251 cities in China 2003-2016, this paper discusses the environmental effects and mechanisms of coordinated urban cluster development. The results showed that the coordinated development of Beijing-Tianjin-Hebei had a significant and continuous effect on the convergence of PM2.5 to low levels. The results of further mechanism identification show that the policy of atmospheric joint defense rules in the process of Beijing-Tianjin-Hebei coordinated development mainly promotes the convergence of PM2.5 through population and economic scale effect, structural effect and technical effect, and promotes the convergence of PM2.5 in the low direction by reducing the economic growth scale of the city itself. It is suggested to further promote the formation and development of Beijing-Tianjin-Hebei integration, strengthen cooperation among local governments in environmental governance e across regions, and promote the green growth and coordinated development of urban clusters.

Exploring the driving forces on sustainable energy and water use in China.

With the rapid growth of global demand for water and energy, the two increasingly restrict economic and social development. The total energy consumption and water use are positively correlated. Identifying the key drivers influencing the energy-water development can realize national resource management and sustainable supplement. In this context, this study aims to capture the key driving forces that affect the sustainable energy-water development characteristics in Chinese change processes throughout 2000-2017. Five driving forces, the EW intensity effect, industrial structure effect, GDP value-added effect, income improvement effect, and population-scale effect, were further decomposed by the logarithmic mean Divisia index (LMDI) model to explore the energy consumption and water use. Our findings indicated that the largest and lowest energy consumers were the manufacturing and construction sectors, while agriculture accounted for the largest share in water use. During the three time intervals, the cumulative effects increased the EW use, but the contributions were declining. Further, these effects had a more prominent influence on water use than energy consumption; GDP value-added effect, income improvement effect, and population-scale effect increased the EW use, while intensity effect played a vital role in decreasing EW use during the study period. Notably, the industrial structure effect had a seesaw role during 2000-2006, which led to a tradeoff between various driving factors. In future sustainable issues, policymakers should pay more attention to energy-saving than water-saving to achieve the national energy and water conservation targets.

Urban and rural differences with regional assessment of household energy consumption in China

This paper explores the crucial factors driving the changes in household energy consumption (HEC) in China during 2005–2017. We propose a decomposition framework based on the Kaya identity and the logarithmic mean Divisia index (LMDI) method to decompose the change in HEC into energy structure effect, energy intensity effect, regional structure effect, per capita consumption effect and population scale effect. We use the model to shed light on the differences of these five factors affecting HEC between urban and rural and among regions while retaining their energy-use characteristics respectively. The results suggest that: (1) Energy intensity and regional structure reduced HEC, whereas population scales, per capita consumption, and energy structure stimulated HEC growth. (2) Rural energy structure contributed larger shares of the increment in HEC. Rural per capita consumption increased generally much more energy consumption than urban counterpart in coastal developed economic regions. Rural population scale curbed the growth of HEC, while urban population scale drove the growth of HEC. (3) Although energy intensity decreased energy consumption at regional level, the differences were found between regions. Moreover, the impacts of regional structure differed significantly between regions, but insignificantly at provincial level. Finally, some policy recommendations will be made based on these suggestive conclusions.

Urbanization-induced spatio-temporal variation of water resources utilization in northwestern China: A spatial panel model based approach

Rapid urbanization has become a common occurrence worldwide, and it is important to ensure a sustainable water supply despite the increasing severity of water resource constraints. As an important area along the one belt and one road line, northwest China has experienced increasing water shortages during the rapid urbanization process in recent decades. In the current work, we employed the Spatial Panel Econometric Model to explore the spatial influence of urbanization on water resource utilization and its driving mechanism (e.g., economy, population, trade, capital, investment and industry structure) based on the water ecological footprint perspective during the period from 2005 to 2017. The results indicated that urbanization and the water ecological footprint showed a spatially clustered trend. In addition, the spatial lag regression coefficient of ρ in the spatial Durbin model was significant at the 1% level, showing that the water ecological footprint had an obvious spatial spill-over effect. Urbanization reduced the water ecological footprint in northwest China, and the main driving factors were the economic scale effect, population scale effect and investment pull effect. However, human capital exhibited a significantly positive relationship with the water ecological footprint, which was contrary to our theoretical hypothesis. In the classification of the water ecological footprint, urbanization reduced the agricultural water ecological footprint both temporally and spatially, while the other classifications all increased. The results are beneficial for informing policies towards sustainable water resource utilization under the framework of integrated development in the process of rapid urbanization in northwest China.

Decomposition of driving factors of industry-related CO2 emissions and its decoupling with economic growth in Fujian Province, China

The emission of CO2 from major sectors and key industries are the predominant sources of regional CO2 emissions. It is the prerequisite to promote sectoral carbon emissions reduction, to cla-rify their influencing factors and investigate their relationship with regional economic growth. It is also of great significance for the implementation of regional total carbon emissions control. Using the Logarithmic mean Divisia index method (LMDI) and the Tapio decoupling model, we analyzed the driving factors, and decoupling status with economic growth of 13 major carbon emissions industries in Fujian Province from 1997 to 2017. The results showed that the electricity and heat production and supply industry was the major source of CO2 emissions in Fujian Province, with an increase of 101.74 Mt (from 18.89 Mt to 120.63 Mt) during the period 1997 to 2017. The top three industries with the fastest annual growth rate in CO2 emissions were non-ferrous metal smelting and rolling processing industry (18.1%), textile industry (12.1%), and ferrous metal smelting and rolling processing industry (12.1%). Among the influence factors for the changes in carbon emissions in 13 major industries, economic growth effect and population scale effect were the main positive driving factors, while the restraining effects of energy structure, energy intensity, and industrial structure were continuously increasing. In terms of decoupling relationship, the decoupling index between economic growth and industry-related CO2 emissions showed a downward trend on the whole. Since the 11th Five-Year-Plan period, some industries had begun to show strong decoupling to some extent. The farming, forestry, animal husbandry, fishery and water conservancy industry exhibited expansive negative decoupling, whereas the electricity and heat production and supply industry exhibited weak negative decoupling during 13th Five-Year Plan period. The effects of energy structure and energy intensity had substantial impacts on the decoupling with economic growth for various industries. The industrial structure effect had a smaller impact on the decoupling with economic growth, while the population scale effect had almost no impact.

Spatial-temporal characteristics and drivers of the regional residential CO2 emissions in China during 2000–2017

Residential sector is an important CO2 emitter in China. This study first analyzes spatial-temporal characteristics of residential CO2 emissions (RCE) of China’s 30 provinces from 2000 to 2017, and then identify the key driving forces of RCE from both temporal and spatial perspectives. Main findings includes: (1) Rural residents experienced a relatively higher growth in RCE than the urban; (2) During 2000–2017 population scale effect contributed most of 35.5% to the increase of RCE while energy structure effect was the key element to reduce RCE by 34.1%. Urbanization effect performed an inverted “U-shaped” evolution trajectory; (3) From the spatial perspective, population scale effect, energy structure effect, energy intensity effect and income improvement effect all worked for enlarging the inequality of RCE among provinces, and their average contribution degree were 31.4%, 23.1%, 22.9% and 16.4%, respectively; (4) In order to attain larger mitigation in RCE, Hebei, Inner Mongolia and Shanxi could focus more on the improvement of energy efficiency and structure while Guangdong, Shandong and Jiangsu on low-carbon consumption and travelling. The improved M-R spatial decomposition model could be applied to investigate city-level RCE, and the results help China to design targeted mitigation measures for provincial residential sector.

Modelling collective cell motion: are on- and off-lattice models equivalent?

Biological processes, such as embryonic development, wound repair and cancer invasion, or bacterial swarming and fruiting body formation, involve collective motion of cells as a coordinated group. Collective cell motion of eukaryotic cells often includes interactions that result in polar alignment of cell velocities, while bacterial patterns typically show features of apolar velocity alignment. For analysing the population-scale effects of these different alignment mechanisms, various on- and off-lattice agent-based models have been introduced. However, discriminating model-specific artefacts from general features of collective cell motion is challenging. In this work, we focus on equivalence criteria at the population level to compare on- and off-lattice models. In particular, we define prototypic off- and on-lattice models of polar and apolar alignment, and show how to obtain an on-lattice from an off-lattice model of velocity alignment. By characterizing the behaviour and dynamical description of collective migration models at the macroscopic level, we suggest the type of phase transitions and possible patterns in the approximative macroscopic partial differential equation descriptions as informative equivalence criteria between on- and off-lattice models. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.