A fractional multi-stage simulation-optimization energy model for carbon emission management of urban agglomeration

<sec><title>Objective</title> The world is still in a phase of rapid industrialization and urbanization. Excessive carbon emissions has become the primary root cause of various urban or even global environmental problems, further impacting human physiological and psychological health. Cities are the largest sources of carbon emissions and are crucial regions for achieving carbon neutrality goals. Urban blue-green infrastructure (UBGI), comprising natural, semi-natural, or artificial green and blue spaces within cities, is considered as the most important carbon sink space in urban areas and has increasingly attracted widespread attention from researchers. However, there are still many unresolved issues regarding the effectiveness of UBGI in carbon sink enhancement and emission reduction: 1) How is the energy efficiency of carbon sink enhancement and emission reduction measured, and what factors influence it? 2) What are the mechanisms and pathways through which UBGI enhances carbon sink and reduces carbon emission? 3) How can UBGI be regulated to better enhance its effectiveness in carbon sink enhancement and emission reduction? 4) What are the limitations and potential directions for future research? This research aims to address these issues and propose scientifically sound planning strategies for UBGI construction to achieve urban carbon neutrality goals. </sec><sec><title>Methods</title> Through literature synthesis and deduction, this research organizes and analyzes the multi-scale measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction, identifies corresponding influencing factors at each scale, and constructs multi-scale planning strategies for UBGI based on the logical framework of “measurement methods–influencing factors – planning strategies”. </sec><sec><title>Results</title> The research proposes UBGI planning strategies across three spatial scales (site, community and urban area), covering three key aspects: Carbon sequestration and sink enhancement, carbon reduction based on temperature reduction (or preservation), and travel-related carbon reduction. Based on current research gaps and planning needs, five major research topics are further identified. This research provides a detailed analysis of the measurement methods and influencing factors of UBGI’s efficiency in carbon sink enhancement and emission reduction from three perspectives: Carbon sequestration and sink enhancement, carbon reduction based on temperature reduction (or preservation), and travel-related carbon reduction. The research finds significant differences in the measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction efficiency across different scales. Contradictory results may occur at different scales, and large-scale research often lacks characterization of internal features, leading to unclear mechanisms of influencing factors and obstructing practical planning. Based on the interpretation of UBGI’s mechanisms for carbon sink enhancement and emission reduction at different scales, this research formulates UBGI planning strategies across three spatial scales (site, community, and urban area). These strategies include: 1) At the site scale, for carbon sequestration and sink enhancement – carbon sink at the source, land balance, and ecological design; for emission reduction – symbiosis with buildings and integration into daily life. 2) At the community scale, for carbon sequestration – overall balance of revenue and expenditure, precise positioning, and proper interconnection of the carbon chain; for emission reduction – incorporation of cool islands and co-construction. 3) At the urban area scale, for carbon sequestration – enhancement of ecological space management and establishment of a carbon-safe pattern; for emission reduction – demand-based layout and organic dispersion. Finally, the research proposes five major research topics for the planning of UBGI’s carbon sink enhancement and emission reduction: How to construct unified measurement methods for UBGI’s efficiency in carbon sink enhancement and emission reduction across scales? How to measure UBGI’s efficiency in carbon reduction based on temperature reduction (or preservation) at the site scale? How to integrate the pathways of carbon sink enhancement and emission reduction for a life cycle assessment of UBGI? How to balance UBGI’s carbon sink enhancement and emission reduction with other functions to achieve the optimal layout for comprehensive benefits? How to achieve urban “carbon justice” through UBGI? </sec><sec><title>Conclusion</title> The carbon sink pathway of the strategy framework requires “carbon sink at the source – precise positioning – safe pattern”, and the emission reduction pathway requires “symbiotic integration – co-construction and sharing – organic dispersion”. The key trade-offs between these two pathways at three spatial scales may provide theoretical support and practical guidance for UBGI construction and management. The five major research topics mentioned above may offer valuable assistance for UBGI construction and future research. </sec>

<p indent=0mm>Cities account for more than 70% of global carbon emissions and play an important role in mitigating climate change and achieving carbon peak and carbon neutrality. As the Paris Agreement emphasizes the need to reach global peaking of greenhouse gas emissions as soon as possible, it is significant to predict carbon emissions at the city level. However, the current COVID-19 pandemic has dramatically impacted global socioeconomic development and carbon emissions, downplaying the reference value for most urban carbon emission prediction models. In fact, existing studies on urban carbon emission prediction have also suffered from some shortcomings, such as unclear analyses of the impact of the pandemic, single scenario prediction, unified setting of growth rates, and failure to provide decision support for the government’s carbon peak work. Therefore, a multi-scenario study on urban carbon emission prediction and carbon peak in the post-pandemic period would provide local governments with scientific data to make their carbon peak action plan. To that end, we set five-carbon emission scenarios: bussiness as usual (BAU), high emissions (HE), extremely high emissions (EHE), low emissions (LE) and extremely low emissions (ELE). Based on the Monte Carlo method, we adjust the probabilities of different periods and different carbon emission scenarios to simulate uncertain evolution of carbon emissions as well as carbon emission reduction. Combining with multi-scenario analyses with the Mann-Kendall trend test and Theil Sen’s trend slope estimation method, we predict carbon emissions of the Pearl River Delta Urban Agglomeration (PRD) from 2021 to 2035 and analyze the evolution path of PRD’s carbon emissions as well as its potential for carbon peak and carbon emission reduction from 2006 to 2035. Discussions are made on the possibility of achieving conditional areas’ carbon peak goal in 2025 in Guangdong and China’s carbon peak goal in 2030. We find that: (1) Carbon emissions of PRD increased rapidly from 2006 to 2016. Dynamic simulation shows that carbon emissions a significant peak in 2020 and decrease to 248.85 M~270.06 Mt in 2035. Carbon intensity decreases by 84.18%–85.21% from 2006 to 2035. Based on the emission reduction of the BAU scenario, the cumulative carbon emission reduction potential of the LE scenario and ELE scenario is as high as 304.86 M and 587.22 Mt from 2021 to 2035. Carbon emission reduction potential based on dynamic simulation of random combination scenario is between −81.68 and 128.25 Mt, with a probability of 67.65% to achieve further emission reduction. The probability of reducing 27.44 Mt carbon emissions is the largest. (2) Shenzhen, Zhuhai, Huizhou and Dongguan are four cities that show an inverted “U” shaped evolution path to achieve carbon peak. All of them reach the carbon peak no later than 2020. From 2006 to 2035, especially after the carbon peak, carbon emissions of these cities will decrease significantly. Their carbon emissions will reduce by 14.15 M–15.40 Mt, 9.17 M–9.94 Mt, 24.07 M–26.08 Mt and 22.36 M–24.24 Mt in 2035, respectively. The cumulative carbon emission reduction potential from 2021 to 2035 is −7.99 M–8.69 Mt, −3.48 M–4.87 Mt, −5.97 M–15.39 Mt and −8.77 M–12.62 Mt, respectively. However, being earlier to reach a carbon peak reduces their carbon emission reduction potential from 2021 to 2035. (3) Guangzhou, Foshan, Zhongshan, Jiangmen and Zhaoqing are five cities that could potentially reach carbon peaks but with divergent evolution paths. Some scenarios are at risk of not reaching a carbon peak. The possibility for Guangzhou, Foshan and Zhongshan to achieve the carbon peak target of conditional areas in Guangdong Province in 2025 is more than 96.01%, while that for Jiangmen and Zhaoqing is less than 20.08%. Moreover, there is a possibility of 2.04% for Jiangmen and Zhaoqing not to reach a carbon peak. In 2035, the emission reduction of the five cities will be 56.90 M–61.87 Mt, 44.35 M–48.16 Mt, 23.92 M–25.91 Mt, 33.78 M–36.58 Mt and 20.15 M–21.88 Mt, respectively. The cumulative carbon emission reduction potential of these cities from 2021 to 2035 is significant, which is −23.75M–26.60 Mt, −17.51 M–<sc>22.17 Mt,</sc> −6.64 M–12.19 Mt, −7.57 M–17.82 Mt and −3.86 M–11.79 Mt, respectively. (4) Being earlier to reach a carbon peak is conducive for cities to reduce carbon emissions. The curve of cumulative carbon emission reduction potential shows that the marginal potential of carbon emission reduction increases with time. So early adoption of emission reduction measures and early realization of carbon peak will promote carbon emission reduction. When making action plans for carbon peak, we should prevent cities from reaching false carbon peak during the platform period, pay attention to the demonstration and acceleration effect of carbon peak cities with relatively high carbon emissions, and explore the carbon emission reduction potential of cities that have difficulties in reaching carbon peak by optimizing their energy structure and utilization efficiency.

In the context of the challenge posed by climate change and the pursuit of the "dual carbon" goals， accurately assessing the efficiency of forest carbon sinks in various regions， identifying the reasons behind efficiency differences， and accordingly proposing effective resource allocation pathways are of great significance for promoting coordinated regional development， improving resource utilization efficiency， and achieving carbon sequestration and emission reduction. Based on provincial data from China spanning from 2004 to 2021， a three-stage DEA model was constructed to measure the forest carbon sink efficiency of 30 provinces and municipalities， and a regional difference analysis was conducted. Simultaneously， a prediction model based on GAN and KOA-CNN-BiLSTM-Attention was established to forecast the total carbon sink targets that each region intends to achieve by 2030， and scenarios were set up to enhance the accuracy of the model. Based on the three-stage theory， further improvements were made to the inverse DEA model to discuss the resource allocation path planning schemes for various regions to achieve their predicted carbon sink targets. In particular， feedback was provided on the current input redundancies and deficiencies in each region. The results showed that： ① Although China's forest carbon sink efficiency showed a trend of growth， it was still at a moderately low level overall. Moreover， there were significant regional differences. Regions with higher efficiency levels were mainly distributed in the southwest and northeast， while those in North China had lower levels and require attention. The remaining regions fell in between and still had room for development. ② The core reasons for the differences in forest carbon sink efficiency among regions were natural resource endowments （including forest resources and natural conditions） and the degree of concern for forestry （including policy support and resource allocation preferences）. Scientific management and operation played a reinforcing role in enhancing the forest carbon sink capacity of the regions. ③ The northeast and southwest regions have made significant contributions in terms of total forest carbon sink. Nationally， the total forest carbon sink is expected to increase by 7% to 30% by 2030， and the forest carbon sink will continue to play a crucial role in carbon emission reduction and sequestration. ④ Each region needs to make incremental improvements in at least one input area. Land input will be the main challenge in achieving future carbon sink targets and is also a key limiting factor for the current level of forest carbon sink efficiency. In the long run， Heilongjiang and Inner Mongolia have the best prospects for forest carbon sink development. The research can provide decision-making references for governments and related industries in pursuing the "dual carbon" goals and help enhance carbon sequestration efficiency and resource allocation efficiency.

A graph-factor-based random forest model for assessing and predicting carbon emission patterns - Pearl River Delta urban agglomeration

As the challenge of global warming intensifies, managing carbon emissions and enhancing carbon sinks have become crucial measures for cities pursuing the ‘dual-carbon’ goal. However, existing research has largely overlooked the complex interrelationship between carbon emissions and carbon sinks. To address this gap, this study constructs carbon footprint pressure (CFP) indicators for nine cities in the Pearl River Delta (PRD) from 2001 to 2021, using carbon sink and carbon emission data. On this basis, we explore the spatiotemporal evolution of CFP in this urban agglomeration by applying a modified gravity model and Social Network Analysis (SNA). The main findings are as follows: (1) The CFP values of the nine PRD cities exhibit a fluctuating upward trend, with significant disparities observed among them; (2) The CFP network demonstrates a dynamically stable yet gradually evolving structure, showing a trend toward flattening over time; (3) The spatial network of CFP reveals a ‘core-periphery’ structure, with Shenzhen, Foshan, and Zhongshan occupying central positions; (4) In terms of block model analysis, inter-block connections are relatively strong, whereas intra-block linkages remain comparatively weak. Based on these findings, this paper proposes relevant policy recommendations.

Tropical and subtropical forests have many valuable roles, one of them within the carbon cycle. Within this cycle they are an essential terrestrial component, functioning as carbon reservoirs and sinks. The importance of (sub)tropical forests in climate change mitigation has been highlighted in recent climate change policies, such as the Paris Agreement, with signatory countries working towards a robust monitoring of their forest carbon stocks and sinks. Additionally, efforts to enhance forest carbon sinks through the restoration of degraded land has been highlighted by the Bonn Challenge, with currently more than 70 pledges in 60 countries underway.Until recently, large-scale assessments and country-level reporting of forest carbon stocks and sinks have been relying on coarse estimates provided in 2006 by the Intergovernmental Panel on Climate Change (IPCC). However, these estimates were based on a handful of studies per global ecological zone (also known as ecozone), did not provide methods for their derivation nor measures of uncertainty, and did not distinguish between forest successional stages.Furthermore, little is known about the drivers of variations in carbon stocks and sinks across (sub)tropical forests, particularly in forests with limited forest plot data availability. In this respect, insights for variations in forests and woodlands in the African dry tropics and in forests recovering from recent disturbance remains limited. Understanding how forest carbon stocks and sinks vary is essential for monitoring greenhouse gas (GHG) fluxes as well as for improving forest conservation and restoration endeavours.Over time, research on (sub)tropical forest carbon stocks and sinks has progressed, as well as country-level monitoring efforts to improve forest GHG reporting. This has led to the increase in availability of forest plot data. Simultaneously, large-scale remote sensing products have become available and region-specific methods for the monitoring of forest disturbance/recovery dynamics have improved over time. Thus, the opportunity to combine forest plot data with remote sensing to evaluate carbon stocks and sinks in (sub)tropical forests at different stages of recovery arises.The overall aim of this thesis is to integrate forest plot data with remote sensing to contribute towards understanding and quantifying aboveground forest carbon stocks (aboveground biomass; AGB) and sinks (aboveground biomass change; ΔAGB) in (sub)tropical forests. More specifically, this thesis has the objectives of (1) improving estimations of (sub)tropical aboveground forest carbon stocks and sinks under varying disturbance types for GHG reporting and of (2) understanding the drivers of aboveground carbon stocks and sinks in recovering forests in the (sub)tropics

Global climate continues to warm; by reducing carbon emission (CE) to cope with climate warming has become a global consensus. The influencing factors of CE exhibit diversification and spatial characteristics, and the complexity of the CE system poses challenges to green and low-carbon development and the realization of China's dual-carbon goals. Taking the Pearl River Delta urban agglomeration as an example, this study explored the influencing factors of CE and designed emission reduction schemes with the help of multi-scale geographically weighted regression (MGWR). Based on this, the system dynamics model was used to construct a CE system framework considering multi-dimensional driving factors, so as to combine the complex CE system with the emission reduction countermeasures considering spatial heterogeneity, and realize the dynamic simulation of CE reduction policies. The results showed that the urban agglomeration as a whole will reach carbon peak by 2025. Shenzhen, Zhuhai, and Dongguan have achieved carbon peak before 2020, while other cities will reach carbon peak by 2025-2030. The government policy constraints can effectively curb CE, but if government constraints were relaxed, CE will rise and individual cities will not reach carbon peak. Comprehensive CE reduction policies are better than a single CE reduction policy. The study found that this model framework provides a systematic analysis of carbon reduction strategies for urban agglomerations, offering decision-makers various combinations of economic development and green low-carbon objectives. This will further contribute to a multi-faceted mitigation of high emission in urban agglomeration and promote regional sustainable development.

With the continuous expansion of the digital economy, its share in China’s overall economy has been steadily increasing. Against the backdrop of the national “dual-carbon” goals, an important question arises: how does the digital economy contribute to carbon reduction? This study selects panel data from nine cities in the Pearl River Delta (PRD) urban agglomeration between 2011 and 2023. The development level of the digital economy is measured using the entropy weight method and an index system. A two-way fixed effects model and a mediation effect model are then employed to empirically examine the relationship and mechanisms between the digital economy and urban carbon emissions. The main findings are as follows: (1) the development of the digital economy exerts a significant negative regulatory effect on carbon emissions, which remains robust after a series of tests; (2) heterogeneity analysis reveals that the inhibitory effect of the digital economy on carbon emissions is more evident in economically advanced cities, and the development level of metropolitan areas significantly influences this relationship; (3) mechanism analysis indicates that stronger environmental regulation significantly enhances the carbon reduction effect of the digital economy; and (4) the scale of e-commerce in the PRD plays a “suppression effect”, offsetting the original carbon-increasing effect of the digital economy and emerging as the key factor underlying its net carbon-reducing impact. Based on these results, the paper provides policy recommendations to better leverage the digital economy in supporting regional carbon reduction.

This paper predicts Net Carbon Emissions of Yunnan form 2007 to 2050 through energy consumption carbon emissions based on economic growth model and forest carbon sinks based on CO2FIX model. Further, we analyze the influencing factors of carbon emissions reduction and the contribution of forest carbon sinks to carbon emissions reduction, and then study low-carbon economy. The curves of energy consumption carbon emissions and net carbon emissions have an inverted "U" type, respectively with a peak value 129.71 MtC at 2035 and 118.89 MtC at 2035. We study carbon emissions from all aspects, including carbon emissions intensity, the carbon emissions, the declining rate of carbon emissions intensity, and the per capita net carbon emissions. Finally, we found that carbon emissions is more than carbon sinks by forests, and net carbon emissions inevitably increase due to the high-speed development of the economy, Afforestation and protecting the original forests can ecologically reduce carbon emissions. Facing the global carbon emissions, we need to develop low-carbon economy and take the path of sustainable development.

SUMMARY A regional energy system consists of diverse forms of energy. Energy-related issues such as utilization of renewable energy and reduction of greenhouse gas (GHG) emission are confronting decision makers. Meanwhile, various uncertainties and dynamics of the energy system are posing difficulties for the energy system planning, especially for those under multiple stages. In this study, an interval multi-stage stochastic programming regional energy systems planning model (IMSP-REM) was developed to support regional energy systems management and GHG control under uncertainty. The IMSP-REM is a hybrid methodology of inexact optimization and multi-stage stochastic programming. Not only can it handle uncertainties presented as intervals and probability density functions but also reflect dynamics of system conditions over multiple planning stages. The developed IMSP-REM was applied to a hypothetical regional energy system. The results indicate that the IMSP-REM can effectively reflect issues of GHG reduction and renewable energy utilization within an energy system planning framework. In addition, the model has advantages in incorporating multiple uncertainties and dynamics within energy management systems. Copyright © 2011 John Wiley & Sons, Ltd.

It is of great significance to formulate differentiated carbon emission reduction policies to clarify spatio-temporal characteristics and driving factors of carbon emissions in different cities and cities at different scales. By fitting nighttime light data (NTL) of long time series from 2000 to 2020, a carbon emission estimation model of Pearl River Delta urban agglomeration at city, county, and grid unit levels was built to quickly and accurately estimate carbon emission in the Delta cities above county level. Combining spatial statistics, spatial autocorrelation, Emerging Spatio-Temporal Hotspot Analysis (ES-THA), and Theil index (TL), this study explored the spatio-temporal differentiation of urban carbon emissions in the Delta and used a geographical detector to determine the influencing factors of the differentiation. The results of the study showed that NTL could replace a statistical yearbook in calculating carbon emissions of cities at or above county level. The calculation error was less than 18.7385% in the Delta. The three levels of carbon emissions in the Delta increased in a fluctuating manner, and the spatial distribution difference in carbon emissions at the municipal and county levels was small. Therefore, a combination of municipal and county scales can be implemented to achieve precise emission reduction at both macro and micro levels. The central and eastern parts of the agglomeration, including Guangzhou (Gz), Shenzhen (Sz), Zhongshan (Zs), and Huizhou (Hz), were a high-value clustering and spatio-temporal hot spots of carbon emissions. Zhaoqing (Zq) in the northwestern part of the agglomeration has always been a low-value clustering and a spatio-temporal cold spot because of its population, economy, and geographical location. The carbon emission differences in the Delta cities were mainly caused by carbon emission differences within the cities at the municipal level, and the cities faced the challenge of regional differences in the reduction in per capita carbon emissions. As the most influential single factor, spatial interaction between economic development and various factors was the main driving force for the growth of carbon emissions. Therefore, the results of this study provide a scientific theory and information support for carbon emission estimation and prediction, differentiated emission reduction measures, and carbon neutrality of cities in the Delta.

The coordinated development of the carbon neutral peak target and dual cycle strategy is an important link to realize the transformation of ecological green and low carbon and also an important carrier of high-quality economic development. Based on the inherent requirements of the synergistic effect of pollution control and carbon emission reduction and high-quality economic development, the coupling mechanism of pollution control and carbon emission reduction and high-quality economic development was discussed. Taking the three major urban agglomerations in China as examples, the comprehensive index system of the synergistic effect of pollution control and carbon emission reduction and high-quality economic development were constructed, respectively. The comprehensive evaluation model, coupling coordination degree model, and panel vector autoregression （PAVR） model were used to empirically analyze the coupling and interaction between the synergistic effect of pollution control and carbon emission reduction and high-quality economic development in the three major urban agglomerations in China from 2010 to 2020. The research showed that： ① The comprehensive development index of pollution control and carbon emission reduction and high-quality economic development showed an overall growth trend. The comprehensive level of pollution control and carbon emission reduction synergy in the Yangtze River Delta urban agglomeration was better than that in the Pearl River Delta urban agglomeration and Beijing-Tianjin-Hebei urban agglomeration. There were significant differences in the level of high-quality economic development among cities, and the overall level was high. ② From the perspective of the coupling relationship, during the study period, the level of reluctant coordination entered the primary coordination level and finally evolved into the intermediate coordination level. The spatial characteristics showed the characteristics of contiguous development centered on cities with higher administrative levels, such as municipalities and provincial capitals. ③ From the perspective of the dynamic relationship, there was a positive interaction between pollution control and carbon emission reduction and high-quality economic development, that is, the development of the two could promote each other. The response of pollution control and carbon emission reduction in the Yangtze River Delta and Pearl River Delta urban agglomerations to the positive impact of high-quality development was higher than that in the Beijing-Tianjin-Hebei urban agglomeration.

Research on the correlation network of carbon emissions and economic between Chinese urban agglomerations

As the largest carbon emitter in the world, China is facing increasing challenge to reduce CO2 emissions. Given this issue, exploring the influencing factors is of great significance for scientific low-carbon emission policymaking. Although previous literature has explored the effects of urbanization on CO2 emissions, the impact of the space of flow on urban carbon emissions have been less explored. Due to the increasing connection between cities, its impact on urban carbon emissions cannot be ignored. Thus, this paper takes the space of flows into account as an aspect of urbanization to supplement the existing literature and empirically examines the multiple effects of urbanization on CO2 emissions in the Pearl River Delta (PRD) urban agglomeration. By using a STIRPAT model, statistical data, and web crawler data, we examined impacts of different types of urbanization on CO2 emissions. Our empirical results show that: (1) Within the PRD urban agglomeration, urban linkage intensity is strongly connected to urban socioeconomic growth, establishing a geographical structure with Guangzhou and Shenzhen as the double core. (2) Our results show that urbanization exerts two opposite effects on CO2 emissions: positively connects carbon emissions with population urbanization, integrated urban linkage flow, and energy intensity, whereas economic urbanization and social urbanization are shown to be negatively correlated. However, spatial urbanization has no significant positive effect on urban CO2 emissions. (3) It is worth noting that urban linkage flows are the second most important factor affecting urban carbon emissions after economic urbanization. Our study could formulate effective planning suggestions for future CO2 emission reduction paths and development modes in the PRD.

A suitable system for estimating changes in carbon storage and carbon emissions from tropical forests was constructed to study the carbon sink function of the Hainan Tropical Rainforest National Park. This article takes Diaoluo Mountain Forest Area as an example. Inventory calculation of forest resources with the help of the calculation method of the provincial greenhouse gas inventory compilation guide. Carbon sink measurement model is constructed in Diaoluo Mountain Forest Area. Changes in forest carbon storage and carbon emissions in five different periods from 1998 to 2018 were calculated. The results show that the forest carbon storage in Diaoluo Mountain Forest Area was 6.4 T, 1.1T, 1.2 T, 1.5 T and 2.1 Tons in the past five periods. Arbor forest has the largest proportion of carbon storage and is dominant; Carbon emissions in the five periods were 497T, 545 T, 263 T, 21T and 19 Tons. The moral is that,the total amount of forest net carbon has increased continuously, arbor forest is an important dominant position of carbon storage,the main gas absorbed and emitted by forest carbon sinks is carbon dioxide,the trend of increasing carbon storage and reducing carbon emissions is significant.It is suggested to continuously improve the community structure and promote the development of the forest carbon sink industry through the establishment of national parks and scientific management. Social capital participates in protecting national parks. This can increase the number of carbon sinks in public rainforest parks.