CO2 Emission Patterns Research Articles

Spatial Characteristic of Coal Production-Based Carbon Emissions in Chinese Mining Cities

The objective of this paper is to investigate CO2 emissions in the production of coal sources at the prefecture level and to analyze their spatial distribution and regional differences based on the spatial autocorrelation and standard deviational ellipse analysis. The results indicate that Chinese coal production from 2018 will most likely generate 485.23 million tons of CO2 emissions, and there still exists an obvious gap between the five coal development districts in terms of their CO2 emissions. A significant clustering pattern and positive spatial autocorrelation are revealed in the coal production-based carbon emissions in China. In addition, the spatial pattern of coal production-based CO2 emissions has an obvious central tendency and directional trend, and the ellipse direction is quite consistent with the Aihui–Tengchong Line. Our findings suggest that energy policy-makers should be concerned about the carbon emission effect when implementing regional coal development plans and actively guide the formation of a low-carbon spatial strategic pattern of coal production with a directional distribution of CO2 emissions perpendicular to the Aihui–Tengchong Line.

FDI inflows, energy consumption and sustainable economic growth for ECOWAS countries: the pollution haven hypothesis approach

This study examined the pollution haven hypothesis for CO2 emissions and ecological footprint with the role of sustainable economic growth, energy consumption, human capital and bio-capacity in six ECOWAS countries using data spanning over 1970 until 2017. Employing a Pooled Mean Group (PMG) technique, the empirical results support the pollution halo hypothesis for CO2 emissions and Pollution Haven Hypothesis (PHH) for ecological footprint. Furthermore, we found evidence of inverted U-shaped pattern of CO2 emissions, which validate the EKC hypothesis, but a U-shaped relationship was found for ecological footprint as indicators of environmental degradation. A unidirectional causal link exists between FDI and CO2 emissions, but a feedback hypothesis is validated between FDI and ecological footprint. The policy implications are that environmentally friendly FDI should be prioritised to achieve a sustainable environment via developing renewable energy.

An agricultural practise with climate and food security benefits: “Claying” with kaolinitic clay subsoil decreased soil carbon priming and mineralisation in sandy cropping soils

As the agricultural sector seeks to feed a growing global population, climate-smart agriculture offers opportunities to concurrently mitigate climate change by reducing greenhouse gas emissions and/or increasing carbon storage in soils. This study examined the potential for clay addition to reduce CO2 emissions from plant residues and soil organic matter in a sandy soil. Soils were sourced from a 15-year-old field trial where claying (200 t ha−1) had already demonstrated improvements in water infiltration, grain yield and profits. Isotopically labelled plant residues (wheat, canola, or pea) were used to separate residue-derived and soil-derived CO2 sources from a nil-clay control, a historically clayed, and two freshly created soils with either high (10%) or low (3%) subsoil clay additions. Laboratory incubations demonstrated that historically clayed soils released less CO2 from plant residues and soil organic matter. Clay addition also decreased the priming effect of adding fresh residue to soils. The results from clay experimentally added in the laboratory varied. Differences in chemical and biological indicators (pH, microbial biomass C and N, extractable organic C and N, NO3−, NH4+, abundance of bacterial, archaeal, fungal, LMCO, GH48 and CbhI genes) did not correlate with patterns of CO2 emissions across treatments. While claying practices have previously demonstrated benefits to crop productivity, this research demonstrates long-term changes in carbon-cycling that could promote greater carbon sequestration.

SPATIOTEMPORAL ANALYSIS ON CO2 EMISSIONS FROM HOUSEHOLDS IN JAPAN

Abstract. Global warming has become worse and worse as the increasing greenhouse gas (GHG) emissions especially by the main contributor carbon dioxide (CO2). Thus, clarifying the spatiotemporal patterns of CO2 emissions from residential sector is very important for policy makers. To support the GHG mitigation in local area, this study provides a bottom-up framework that could count the monthly residential CO2 emissions at community level, demonstrated for Japan. A map-based population census is utilized to count the monthly and yearly emissions by combining the statistics data on households with detailed emission intensities. The residential emissions from each census area are estimated and mapped by Geographic Information System. Through the analysis, we proposed the solutions on GHG mitigation and reported the spatiotemporal patterns for residential emissions.

Analyzing Temporal and Spatial Characteristics and Determinant Factors of Energy-Related CO2 Emissions of Shanghai in China Using High-Resolution Gridded Data

In this study, we create a high-resolution (1 km x 1 km) carbon emission spatially gridded dataset in Shanghai for 2010 to 2015 to help researchers understand the spatial pattern of urban CO2 emissions and facilitate exploration of their driving forces. First, we conclude that high spatial agglomeration, CO2 emissions centralized along the river and coastline, and a structure with three circular layers are the three notable temporal–spatial characteristics of Shanghai fossil fuel CO2 emissions. Second, we find that large point sources are the leading factors that shaped the temporal–spatial characteristics of Shanghai CO2 emission distributions. The changes of CO2 emissions in each grid during 2010–2015 indicate that the energy-controlling policies of large point emission sources have had positive effects on CO2 reduction since 2012. The changes suggest that targeted policies can have a disproportionate impact on urban emissions. Third, area sources bring more uncertainties to the forecasting of carbon emissions. We use the Geographical Detector method to identify these leading factors that influence CO2 emissions emitted from area sources. We find that Shanghai’s circular layer structure, population density, and population activity intensity are the leading factors. This result implied that urban planning has a large impact on the distribution of urban CO2 emissions. At last, we find that unbalanced development within the city will lead to different leading impact factors for each circular layer. Factors such as urban development intensity, traffic land, and industrial land have stronger power to determine CO2 emissions in the areas outside the Outer Ring, while factors such as population density and population activity intensity have stronger impacts in the other two inner areas. This research demonstrates the potential utility of high-resolution carbon emission data to advance the integration of urban planning for the reduction of urban CO2 emissions and provide information for policymakers to make targeted policies across different areas within the city.

Carbon dioxide emissions: Spatiotemporal variation in a young and mature riparian forest

Forested riparian zones in agricultural landscapes provide multiple ecological services including nutrient abatement and carbon (C) sequestration. However, limited information exists on CO2 emissions from rehabilitated riparian forests, which could negate their C sequestration potential. The objective of this study was to evaluate temporal and spatial variation of CO2 emissions, and its relationship to soil and vegetation characteristics, in a 30-year old riparian zone rehabilitated with trees (RH) compared to a 150-year old undisturbed natural (UNF) riparian forest. Soil moisture, temperature, C, total nitrogen (TN) and vegetation characteristics (light intensity, distance to the nearest tree) were significantly (p < 0.05) different between RH and UNF sites. Soil CO2 emissions were significantly greater (p < 0.05) in the RH (27 mg CO2-C m−2h−1) than in the UNF (18 mg CO2-C m−2h−1) riparian forest. Both riparian forests exhibited strong seasonal patterns of CO2 emissions with significantly greater (p < 0.05) emissions during the summer. Carbon dioxide emissions were significantly different (p < 0.05) with respect to location (perpendicular and parallel) from the stream. For example, emissions were significantly different (p < 0.05) among perpendicular locations in the RH site, but among parallel locations in the UNF site. Emissions of CO2 were significantly correlated (p < 0.05) to soil moisture and temperature in the RH site, and significantly correlated (p < 0.05) to temperature, SOC and TN content in the UNF site. Carbon dioxide emissions were not significantly correlated (p < 0.05) to vegetation characteristics. We concluded that the younger RH site generated contrasting soil and vegetation characteristics that influenced spatial CO2 emissions differently compared to a mature riparian forest. However, our study also found that seasonal changes had a stronger influence on CO2 emissions than spatial variability in both riparian forests.

Adaptive comfort assessments in urban neighbourhoods: Simulations of a residential case study from London

A warming climate, increasing frequency and severity of extreme heat events, and the heat island effect are cumulatively expected to exacerbate climate thermal loading on urban buildings. This in turn could lead to increased summertime overheating risk, with any active means for addressing this likely to influence future energy consumption and CO2 emission patterns. This paper examines how the microclimatic loading presented by the heat island (UHI) effect influences summertime adaptive comfort in traditional urban residential buildings. The method for addressing this utilises the novel approach of coupling a computationally efficient urban climate model with an established building energy model to simulate a residential street canyon within the London heat island. Key findings highlighted adaptive capabilities to achieve summertime ‘comfort’ in most rooms without the need for energy intensive mechanical cooling. The alternative of using indiscriminate and widespread mechanical cooling within the canyon length is estimated to result in a 0.4 K increase in nocturnal canyon temperatures, and an additional 4.4% of CO2 released to the climate. In contrast, a targeted approach of cooling rooms where adaptive capacity is insufficient more than halves the canyon CO2 emission estimate; which in turn highlights the necessity for detailed overheating assessments in managing energy use in such traditional residential neighbourhoods within UHIs.

Assessment of the regional fossil fuel CO2 distribution through Δ14C patterns in ipê leaves: The case of Rio de Janeiro state, Brazil

Fossil fuel-derived CO2 (Cff) emission patterns and their point sources across the Rio de Janeiro megacity and state were estimated from a single regional-scale Δ14C distribution map based on isotopic measurements of ipê leaves (Tabebuia, a popular flowering deciduous perennial tree). Data from multi-year sampling (i.e., 2014–2016) was renormalized to reflect 14C signatures of the 2015 calendar year. Spatial variability in Δ14C ranges from a maximum of 27.1 ± 0.4‰ (city of Petrópolis, a higher-elevation municipality) to a minimum of −43.6 ± 1.4‰ (i.e., approximately 27.6 ± 1 ppm of Cff — Santo Cristo, a district within the Rio de Janeiro city). Overall, higher Δ14C values correlate well with green habitats and high elevation areas, while lower values are associated with Cff emissions in densely populated areas with higher industrial and traffic footprints. Cff emissions are higher where local air circulation is poor, such as the area surrounding Guanabara Bay. Other areas with significantly higher Cff emissions were the Paraíba Valley and Mountain regions. These results may be explained by atmospheric transport of CO2 from neighboring states, such as São Paulo and Minas Gerais, and by the predominant west winds and the limited regional air flow created by large topographic features. Lower Cff emissions were observed in the Northwest and Lakes regions, which are dominated by agriculture and tourism activities. Our results highlight the potential of directly estimating Cff for studying urban landscapes in the southern region of Brazil through 14C time-integrated distribution mapping of ipê leaves. The method could also be used to augment greenhouse gas (GHG) emissions inventory studies trends in partitioning Cff from CO2 of bio-template sustainable sources.

CO2 emissions patterns of 26 cities in the Yangtze River Delta in 2015: Evidence and implications

As a country with the highest CO2 emissions and at the turning point of socio-economic transition, China's effort to reduce CO2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran's I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran's I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city.

Microbial communities from different soil types respond differently to organic waste input

Using organic waste products (OWP) in agriculture has been reported to impact both the activity and composition of soil microbial communities. However, little information is available on how the response of a soil microbial community to a given OWP may depend on the physicochemical and microbial properties of the soil receiving the input. Here, we performed a microcosm experiment to compare the effect of 2 different OWPs (GWS: co-compost of Green Wastes and Sewage sludge or FYM: FarmYard Manure), each applied to 5 different soils, on the activity, abundance and diversity of the soil microbial communities. Soils were selected to represent a range of physicochemical and climatic characteristics. CO2 and N2O emissions, microbial biomass and taxonomic diversity were monitored for 28 days following OWP input. The five soils presented different prokaryotic and fungal communities structures before OWP application. During the 28 days of incubation, those control soils (without OWP) harboring the highest organic matter contents released the greatest CO2 and N2O emissions, and had the highest soil microbial biomass. The impact of organic amendments on soil activity and microbial diversity differed with the nature of the OWP. FYM application increased CO2 emissions 2-fold and delayed N2O emissions compared to GWS. Major changes in prokaryotic genetic structures were also observed when GWS was applied. The effect of OWPs was dependent on soil type and the five soils exhibited distinct patterns of CO2 and N2O emission after a given input. This accorded with the fact that the structure and composition of the microbial communities harbored by each soil type responded differently to a given OWP application. To conclude, our results show that different soil types, harboring distinct microbial community structures, responded differently to OWP application, leading to different patterns and rates of greenhouse gas emissions. This response was also OWP-dependent.

CO2 emission patterns in shrinking and growing cities: A case study of Northeast China and the Yangtze River Delta

The implementation of CO2 emission mitigation policies in cities is the key to China achieving its national emission mitigation targets. China is experiencing rapid urbanization and facing huge inequality in regional development and then shrinking cities generate. This study, for the first time, discusses long-term CO2 emission patterns of shrinking cities with comparisons of growing cities. 55 cities in Northeast China and the Yangtze River Delta are selected as cases. We first categorize these cities into three groups of shrinking cities and three groups of growing cities with a population index. Each group’s emission patterns in terms of energy, employment and industry structures are then examined. We find that CO2 emissions in the rapidly shrinking group presented a continuously increasing trend, while the other five groups reached their emission peaks in 2011–2013. For slightly and moderately shrinking groups, CO2 emission mitigation was a positive sign but occurred with the decline of secondary industry, especially for resource-based or heavy manufacturing cities, such as Daqing and Anshan in Northeast China. In the case of three types of growing cities, cities were capable of mitigating CO2 emissions and maintaining economic growth. The slightly growing group was the optimal type among these six groups. Its CO2 emissions experienced a decline with an annual rate of −1.47% during 2013–2015, while the economy still soared (increased by 7.27% annually). New economic growth points should be fostered to mitigate further shrinkage and achieve sustainable development for shrinking cities. The cities’ categorization rules, research thinking, and results offered in this study could provide a reference for other cities or developing countries at similar industrialization/urbanization phases to abbreviate their path towards a low-carbon economy.

The Spatiotemporal Pattern of Decoupling Transport CO2 Emissions from Economic Growth across 30 Provinces in China

Since 2005, China has become the largest emitter of CO2. The transport sector is a major source of CO2 emissions, and the most rapidly growing sector in terms of fuel consumption and CO2 emissions in China. This paper estimated CO2 emissions in the transport sector across 30 provinces through the IPCC (International Panel on Climate Change) top-down method and identified the spatiotemporal pattern of the decoupling of transport CO2 emissions from economic growth during 1995 to 2016 by the modified Tapio’s decoupling model. The CO2 emissions in the transport sector increased from 103.10 million ton (Mt) in 1995 to 701.04 Mt in 2016. The year, 2005, was a turning point as the growth rate of transport CO2 emissions and the intensity of transport CO2 emissions declined. The spatial pattern of transport CO2 emissions and its decoupling status both exhibited an east-west differentiation. Nearly 80% of the provinces recently achieved decoupling, and absolute decoupling is beginning to take place. The local practices of Tianjin should be the subject of special attention. National carbon reduction policies have played a significant role in achieving a transition to low-carbon emissions in the Chinese transport sector, and the integration of multi-scale transport CO2 reduction policies will be promising for its decarbonisation.

What determines the diversity of CO2 emission patterns in the Beijing-Tianjin-Hebei region of China? An analysis focusing on industrial structure change

As the largest CO2 emitter in the world, China is facing significant challenges in CO2 mitigation. In the search for low-carbon development and industrial upgrading, the Beijing-Tianjin-Hebei (BTH) region represents an experimental plot for all of China. In this study, we employed a comprehensive analytical framework to investigate the changing pattern of CO2 emissions in the BTH region, where large-scale industrial structural changes are happening; the results of this study provide a critical reference for regions facing the dual tasks of industrial transitions and environmental protection. The results indicate that industrial structural changes have helped diminish mounting CO2 emissions in the BTH region; however, the extent of the effect differs among the provinces involved. Improvements in technology and organization level in the fastest growing sectors, such as machinery manufacturing and service industries, have helped to curb CO2 emissions significantly. However, industrial structural changes have failed to narrow the CO2 emission gaps among provinces in the region. Being in different phase of development and possessing various resource endowments, the provinces in the BTH region have followed a diversified path of industrial upgrading and have emerged as a region with explicit labor divisions and supply chains. On one hand, this intensifies regional CO2 emission differences, and on the other, it has allowed policymakers to promote more customized decarbonizing plans.

Structural patterns of city-level CO2 emissions in Northwest China

In Northwest China, quantifying city-level CO2 emissions is fundamental to CO2 alleviation but encounters difficulties in data availability and quality. Further, structuring city-level emissions could be conductive to CO2 reduction. This study applies a practical methodology to 16 northwestern Chinese cities to grasp their historical trajectories of CO2 emissions. Then, structuring CO2 emissions is explored in terms of industrial structure, energy mix and urban-rural disparities for 8 northwestern Chinese cities. Results show that: (1) for 16 cities (2010–2015), capital and industrial cities generated most emissions. Meanwhile, CO2 emissions were mostly incompatible with CO2 intensity, but consistent with CO2 per capita; (2) for 8 cities (2006–2015), energy producing sectors, heavy manufacturing sectors, and coal remained major drivers of emissions. Then, the interconnection between industrial structure and energy mix exerted temporally varying impacts on emissions from energy producing sectors and heavy manufacturing sectors. Besides, urban gas consumption and rural coal use continued affecting most of household consumption emissions and household consumption emissions per capita. Moreover, the interplay between emissions and population was changed when emissions by energy type were decomposed among urban and rural households; and (3) uncertainty results fall in the range of −39% to 6%. Finally, implications for CO2 reduction and future work are proposed.

Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage

Energy consumption from central heating has rapidly increased in the cities of the North China Plain (NCP). The increasing use of natural gas in the central heating supply system may have altered the spatial and temporal patterns of CO2 emissions from central heating, yet the quantitative impacts are poorly understood. Here we detect the spatio-temporal dynamics of CO2 emissions of central heating from 2012 to 2016 at the prefectural-city level in the NCP region, by using the satellite NPP-VIIRS nighttime light data and a panel regression model to estimate CO2 emissions on a 5 × 5 km2 grid. We find that despite a slight decrease (2%) in 2014 under the “Natural Gas Utilization Policy”, CO2 emissions continued to grow. Between 2012 and 2016, CO2 emissions from central heating in the NCP increased from 106 to 121 Tg, although CO2 emissions declined by 12% in Beijing due to the increasing contribution of natural gas boilers. The gridded CO2 emissions map shows that over 2012–2016 coal burning is the main driving force of CO2 emissions in both urban and non-urban regions, despite the increasing fraction of gas-based heating. Our results contribute to city-level policymaking on carbon reduction and climate change mitigation. The high-resolution gridded CO2 emissions can also be applied in physical models to facilitate carbon cycle studies.

Realizing low-carbon development in a developing and industrializing region: Impacts of industrial structure change on CO2 emissions in southwest China

China is undergoing rapid industrial structure change, resulting in great disparities in industrialization stages and CO2 emission patterns across regions. In this study, we focused on the southwest economic zone (including Chongqing, Sichuan, Guangxi, Yunnan, and Guizhou), which is in the middle-industrialization stage and aims to mitigate CO2 emissions during strategic industrial transformation. We applied a framework with refined indicators based on input-output analysis (IOA) and structural decomposition analysis (SDA) to characterize and quantify the impact of industrial structure change on CO2 emissions during 2002–2012. In this period, construction ranked first in CO2 emission due to relatively high-carbon production structure and increasing share in final demand, which increased construction related CO2 emissions. Furthermore, we found that diversification in development and competitive industries had different impacts on CO2 emission trends. Yunnan and Guizhou experienced a transition from light manufacturing to resource-related manufacturing; therefore, CO2 emissions in resource-related manufacturing showed an increasing trend due to the extensive production structure in the two provinces, while it showed a decreasing trend in the other three provinces. Moreover, Chongqing, Sichuan, and Guangxi showed an apparent expansion trend in machinery manufacturing and related CO2 emissions, driven by investment, final demand structure, and production structure changes. Meanwhile, this zone avoided large-scale CO2 emissions in these sectors through net imports, by making good use of geographical advantages and trade structure. The CO2 emissions of the service sectors showed increasing trends due to increasing proportions in consumption structure and final demand structure. Furthermore, this led to a transition from consumer services to producer services with expansion. Among the five provinces, Chongqing had the lowest-carbon development pattern, owing to its higher technical levels of manufacturing and relatively lower-carbon production structure, which could be a reference point for rapid low-carbon economic development under middle industrialization for the other provinces or regions with the similar industrial characteristics.

CO2 emissions patterns in 7 top carbon emitter economies: The case of transport sector

These days, climate change issues and their impacts on the economy, social and natural system of countries have become the main concern of worldwide, even for those countries that are facing major economic problems. These issues arise from the combustion of fossil fuels. One of the major contributors to global CO2 emission is the transport sector. Therefore, in order to identify the trends and characteristics of transport carbon emissions this study reviews the relevant literature significantly; and decomposes carbon emission in this sector in 7 top transport CO2 emitter countries by applying the logarithmic mean Divisia index (LMDI). The results indicated that, during 2000–15, transport carbon emissions increased in the United States, China, India, Canada, Russia, and Brazil whereas decreased in Japan. Hence, in the majority of these countries, energy conservation strategies were not successfully implemented during the study period. In the majority of countries, the main contributor to CO2 emission reductions was carbon intensity, whereas the main drivers of CO2 emission increases were electricity structure and economic output effects. Reductions in carbon emissions could be achieved by further optimizing energy structures, limiting the private vehicle population. A set of policies and regulations, from demand-side and supply-side, must be implemented to be effective in reducing GHGs emissions.

Forecasting the spatiotemporal variability of soil CO2 emissions in sugarcane areas in southeastern Brazil using artificial neural networks.

Carbon dioxide (CO2) is considered one of the main greenhouse effect gases and contributes significantly to global climate change. In Brazil, the agricultural areas offer an opportunity to mitigate this effect, especially with the sugarcane crop, since, depending on the management system, sugarcane stores large amounts of carbon, thereby removing it from the atmosphere. The CO2 production in soil and its transport to the atmosphere are the results of biochemical processes such as the decomposition of organic matter and roots and the respiration of soil organisms, a phenomenon called soil CO2 emissions (FCO2). The objective of the study was to investigate the use of neural networks with backpropagation algorithm to predict the spatial patterns of soil CO2 emission during short periods in sugarcane areas. FCO2 values were collected in three commercial crop areas in the São Paulo state, southeastern Brazil, registered through the LI-8100 system during the years 2008 (Motuca), 2010 (Guariba city), and 2012 (Pradópolis), in the period after the mechanical harvesting (green cane). A neural network multilayer perceptron with a backpropagation algorithm was applied to estimate the FCO2 in 2012, using data from 2008 and 2010 as training for the neural network. The neural network initially presented a mean absolute percentage error (MAPE) of 18.3852 and a coefficient of determination (R2) of 0.9188. Data obtained from the observed and estimated values of FCO2 present moderate spatial dependence, and it is observed from the maps of the spatial pattern of the CO2 flow that the results from the neural network show considerable similarity to the observed data. The model results identify the higher and lower characteristics in sample points of CO2 emissions and produce an overestimation of the range of spatial dependence (0.45m) and an underestimation of the interpolated values in the field (R2 = 0.80; MAPE = 12.0591), when compared to the actual soil CO2 emission values. Therefore, the results indicate that the artificial neural network provides reliable estimates for the evaluation of FCO2 from data of the soil's physical and chemical attributes and describes the spatial variability of FCO2 in sugarcane fields, thereby contributing to the reduction of uncertainties associated with FCO2 accountings in these areas.

Discovering the patterns of energy consumption, GDP, and CO2 emissions in China using the cluster method

This paper explores the effects of enforced Chinese policies regarding environmental matters at the provincial level. The paper proposes several features and explores datasets from the last four Five-Year Plans, which extensively covered the period from 1995 to 2015.As the datasets are multidimensional, dimensional reduction is applied with a control of information loss described by data variance preservation. A data-based clustering algorithm is applied to identify the similarities, and characteristics are explored in relation to the specific features.The use of this method makes it possible to identify the changes in the relevance of the features in the most recent Five-Year Plan, in which decarbonization is relevant instead of productivity. This method also makes it possible to identify specific efforts several provinces have made to introduce cleaner energy production.Following the analysis, it is concluded that the proposed features enable the proposed method to be considered a tool for analyzing the real-world effects of the adopted policies.

Beach-cast seagrass wrack contributes substantially to global greenhouse gas emissions

Seagrass ecosystems have received a great deal of attention recently for their ability to capture and store carbon, thereby helping to mitigate climate change. However, their carbon-sink capacity could be offset somewhat if exported plant material – which accounts for ∼90% of total leaf production – undergoes microbial breakdown and is emitted into the atmosphere as a greenhouse gas. Here we measured emissions (CO2 and CH4) from the breakdown of exported seagrass plant material, focusing on beach-cast ‘wrack’. We tested two seagrass species; Zostera nigricaulis and Amphibolis antarctica, which have contrasting morphologies and chemistries. We found that both species of wrack were substantial sources of CO2, but not CH4, during the decomposition process. Biomass loss and the coinciding CO2 emissions occurred over the 30-day experiment, and the pattern of CO2 emissions over this time followed a double exponential model (R2 > 0.92). The initial flux rate was relatively high, most likely due to rapid leaching of labile compounds, then decreased substantially within the 2–9 days, and stabilizing at < 3 μmol g−1 d−1 during the remaining decomposition period. Additionally, seagrass wrack cast high up on beaches that remained dry had 72% lower emissions than wrack that was subjected to repeated wetting in the intertidal zone. This implies that relocation of seagrass wrack by coastal resource managers (e.g. from water's edge to drier dune areas) could help to reduce atmospheric CO2 emissions. Scaling up, we estimate the annual CO2-C flux from seagrass wrack globally is between 1.31 and 19.04 Tg C yr−1, which is equivalent to annual emissions of 0.63–9.19 million Chinese citizens. With climate change and increasing coastal development expected to accelerate the rate of wrack accumulation on beaches, this study provides timely information for developing coastal carbon budgets.